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Towards Stronger Causal Claims in Management Research

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Joep Cornelissen addresses widespread concerns about the reliability and strength of many causal claims made in management research.

Image courtesy of interviewee. May 7, 2024

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This video video discusses the significance of understanding causal relationships in management research and social sciences. It emphasizes that establishing clear causal links between events (where condition A reliably leads to effect B) is crucial for informing policy and practice. The speaker identifies three main approaches to studying causation:

1. **Propositional Approach**: This video video method focuses on direct cause-effect scenarios, where researchers hypothesize and test whether a specific condition leads to an effect, generally using controlled interventions.

2. **Configurational Approach**: This video video method counters the propositional perspective by examining the various conditions and processes that might lead to a specific outcome. It acknowledges the complexity of phenomena and looks at multiple potential causes rather than assuming a root cause.

3. **Process Approach**: This video video approach emphasizes the importance of understanding how events unfold over time, focusing on the sequence of events that lead to particular outcomes, thus highlighting the temporality of causation.

The speaker critiques that while each approach has its strengths—such as the propositional method's ability to show cause and effect clearly or the configurational method's comprehensive view of potential causes—they also have limitations, particularly in dealing with the complexity and interdependence of causal relationships.

This video video suggests a "causal triangulation" method, advocating for the use of all three approaches in conjunction, which would compensate for the limitations of each and lead to more robust causal inferences. It argues that leveraging diverse methods will enhance theoretical explanations and improve practical applications in management and social sciences.

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X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

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Causal relationships,

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have been a concern within management research

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and across the social sciences for, for a long time.

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Uh, and of causal relationship is really the idea that for

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the events are happening in the world that we study,

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we can really say that one, condition A is seen

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to produce a particular effect, b

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and that seemed to be important to be able

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to establish those causal relationships so

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that in no uncertain terms

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and beyond basic correlations, we can say a will on

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in general lead, lead to B,

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either in a very direct deterministic sense that A, always

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B always follows from a, the one condition,

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or the one effect from, the base condition,

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or maybe in a more probabilistic sense

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that a on average is more likely to, to lead to B.

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So this has been a ma a main preoccupation of,

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many social scientists, including management research

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for a very long time.

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Also, with the perspective that if we, if we're able

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to properly identify these causal relationships,

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we can also more effectively inform policy

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and practice on the things that, managers

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or policy makers might, might do.

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Yeah. What is interesting is that the topic of causation,

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and also questions around causal relationships,

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and also the idea that we need to be really certain, be able

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to make a robust inference as

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to whether a causal relationship is there.

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These types of questions haven't really been

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su substantively discussed, to any great extent.

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So most of the discussion

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that has taken place is really at the level of methods

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where, typically by importing, techniques

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and methods and research designs from econometrics

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or from sociology, people have made assumptions about

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what causal relationships are

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and how they can following these methods,

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be properly identified in each of these different contexts.

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But what is important to remember here is that,

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if you compare, let's say, a experimental design

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with a cross case comparative analysis method,

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the assumptions that are being made about what causation is,

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what a causal relationship is,

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and the criteria that are also coming with these methods as

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to how to make a causal inference, how to make an inference

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that a causal relationship is there,

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are actually very different.

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Uh, and that discussion hasn't really taken place yet.

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And I think it's important to first figure that out

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how we generally think about causal relationships,

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alongside applying some of these methodological techniques.

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Um, yeah, that's, it is a very good question.

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So in the main, I've identified

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that there are three ways in which we tend to approach

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questions of causation

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and tend to approach the, the issue of how

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to identify cause relationships.

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So there's a propositional approach, which is basically

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what it says where researchers

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Hypothesized that, two events

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or, happenings are related

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as a simple cause effect scenario.

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So one condition, A, directly effects B,

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either in a direct way or through some mediating path.

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So through c having that ultimate effect, on B.

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So you have these very simple if done scenarios where

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researchers subsequently do their best

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through their research designs

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and through their, intervention techniques to make sure

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that in a very controlled manner,

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they can actually establish

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that b in no uncertain terms follows from, from a,

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so that's the propositional approach.

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Then we have the configurational approach,

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which actually emerged as, an antidote

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or a challenge to the propositional approach,

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where the propositional one

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is really looking at a root cause or root condition,

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or presupposes a root cause or root condition,

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and then looks at the subsequent effect In a very simple

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sort of causal scenario, the configurational approach says,

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well, actually, what might be better given

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that many phenomena are very complex, is to look at

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a particular outcome or effect that has materialized

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and then look backwards at all the sets of conditions

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and processes along, across those conditions

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that might have brought that particular effect about.

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So it's a very different approach where instead

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of looking at the effects of presupposed causes, you look at

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what might be the potential causes

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or the potential conditions that might have caused,

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a particular effect.

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And it's as an approach also, compared

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to the propositional one, much more alive to

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a complex causal field, a whole range of conditions

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as potential causes

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that might have brought the effect about.

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So your frame of reference, is enlarged,

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but you also with that look at

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multiple conditions alongside one another, and you try

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and figure out through your reasoning

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and through your probing of what you found.

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Uh, so typically these, this is post case comparisons

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that you, or,

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or comparative research design that you use across cases

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or across data sets where you try

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and not just identify these potential conditions,

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but also counterfactually probe, which of the ones

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or which of the sets of conditions alone

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or together might have brought this effect about.

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So it's a much more systemic form of causation compared

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to the, the propositional approach.

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And then finally, the process approach, is,

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is again, another approach.

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So it says, instead of listing sets

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of conditions on the one hand

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and effects on the other, either in a very simple way

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with the propositional approach

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or in a more complex way with a configurational approach,

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what we should be doing instead is

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to follow in a more longitudinal sense how hap

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how events play out over time so that we can really see

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what is the sequencing of events

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that might have materialized in a particular case

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or cross cases, and maybe also across larger data sets,

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if we have that longitudinal, frame of reference

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for those data to see

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what effect materialized in that particular case.

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So it's a, an approach that takes temporality

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and takes the longitudinal perspective very seriously,

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and is also alive to the very uncertain probabilistic nature

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of the world where sometimes effects get brought about

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through certain sequences of events that, that happen.

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So they have their own, that approach comes

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with its own methodology, it's longitudinal,

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but there's also various techniques that you can use to,

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to bracket sequences of events to see the transition points

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between these, these sequences and to try

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and decipher a particular causal process running,

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alongside that trajectory.

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Yeah, I think, I think the most interesting thing is that,

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so each of these approach approaches obviously comes

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with its own, prepositions of how to

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identify causal relationships.

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And it also makes very different assumptions about

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where we find these causal relationships and where

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and how causation happens.

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And one, obvious observation is,

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and there's a philosopher called Nancy Cartwright, who

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among many others has, has spotted this observation, is

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that some approaches are really looking at, so she calls

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that the really, really, using causes

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or manipulating causes.

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So think of the propositional approach where you say, okay,

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I'm assuming this is a, a root cause

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or a true cause that will bring a certain effect about

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either in a deterministic or in a more probabilistic sense.

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Then I'm trying to find research designs

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and data sets that allow me to manipulate

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or in a very controlled manner, see whether that's the case.

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And then, so it's really the look looking at the effects

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of presumed cause then the other approaches, so the process

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and configurational are actually the opposite,

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so it's more hunting for causes.

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So I know that these types of effects

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as phenomena generally occur in the world.

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Let's look back and see what might have been,

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all the sets of conditions and,

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and potential causes, in those terms

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that I brought it about.

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So it's more hunting for causes, trying to identify and,

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and weed out the sets of conditions, that may have,

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may have not mattered too much.

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Then the sets of conditions that we now can say

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are actual causes that produce this, this outcome.

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So it's, it's as an approach compared to the using

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or manipulating causes

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and looking at these very controlled intervention,

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experimental based designs that match that.

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It's as an approach taking a very different, stance

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and looking more at the world out there, so phenomena

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that can be observed effects

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or outcomes that can be observed

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and then be reasoning backwards.

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So I think that's a very fundamental distinction,

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and it's actually very helpful to think about it

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because it also allows you to say

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where the one comes with a blind spot.

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You know, if I only reason forward

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and presume that condition A will BA root cause

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and will in most instances lead to B, well,

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maybe the reality is a bit more complex

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and we have something to learn from the other approach.

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Uh, not to say that, you know, I'm not trying

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to favor the one over the other,

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but, the point with the triangulation idea is to say

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that they all come with different vantage points.

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They all come with different strengths and limitations

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and also different criteria for making, an influence

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around the causal relationship.

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So let's join them up, let's see

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what they collectively offer so

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that we get a more robust influence as

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to whether causal relationship is there, and can

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accordingly theorize better come up

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with stronger explanations, but also inform policy

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and practice in a, in a much more effective way.

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Uh, and to back that up, in the paper, I show how some

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of the classic criteria for how we think about, for

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how we think about a cause of relationship, so what a cause

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of relationship needs to have in order for us

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to talk about not just the correlation,

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but a course of relationship, how these criteria are not

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satisfied by one approach,

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but they're more likely to be satisfied

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by multiple approaches.

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So the propositional one is very good

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with its controlled intervention methodology to try

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and identify whether a course temporally precedes an effect.

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So the course or precedence that is a classic criteria of,

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of acausal relationship is, is something that

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that approach can do really well,

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but it also has a lot of limitations.

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I already noted the lack of its ability to deal

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with complexity, the lack of its ability to deal

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with spurious causation.

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The lack of its ability to really beyond doubt show

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that there is a dependent relationship between A and B.

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So B will necessary

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and sufficiently follow from, from a, those are all things

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that it struggles with.

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Whereas the other approaches then have,

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have equity strengths and limitations.

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So the process one is very good at, as I described it,

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to trace the, the real transitive causal process that goes

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through the sets of conditions,

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or the events that you've, you've categorized as conditions

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or potential causes over time.

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So tracing that through.

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So the classic notion that we can speak of causation when A,

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produce an, an effect,

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and B, b produce an effect C, you can trace that

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or be much closer to that with the process approach than,

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with the other two approaches.

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But equally, the process approach also has its limitations,

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and again, causal dependence to really showing that b

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depends on a, is very difficult to establish in

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and of itself by that approach.

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And then the configurational approach is very good in trying

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to force yourself to look at the world out there,

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not limit yourself to one presupposed condition

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or limited set of conditions as potential causes,

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but really look at the world out there.

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What are the regularly occurring connections

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between potential causes, conditions as causes,

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and the effects that that materialize.

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So it has this classic criteria of regularity, in,

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in the events that we observe

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and the connections between them, which is, which has

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for a long time been suggested to be one of the key criteria

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of a, of a course of relationship.

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Uh, it does that really well.

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Uh, but for its bite, it also has its own limitations

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because it is, it gives you those regularly,

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occurring connections,

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but then again, it, it struggles to deal

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with cause of dependence.

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There may be spurious correlations in there.

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And, and this is again,

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where the other approaches in combination can compliment

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and offset some of the limitations,

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that each of them have.

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Um, I think the, the future, so the,

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the future implication of this is really around,

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methodological practice, so that as social scientists

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and as management researchers,

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we should really embrace this idea that if we want

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to make robust calls

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or in infants, there's not one single route is not

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what single approach to cause and identification,

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but we really benefit when we, in our,

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inquiries into particular phenomena make the most

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of these different approaches together

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and use them alongside each other in ways

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that offset the biases and limitations of the one approach,

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but also in, in simultaneously complement each other in,

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in moving towards these more robust causal in infants.

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Um, so that is the idea with causal triangulation, where

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previously we talked a lot about causal identification,

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and that's an approach that primarily comes out

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of econometrics

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and is a set of techniques that fit

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with the propositional approach to theorizing,

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that has its, its its value and its advantages,

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but as I've explained, is still, limited

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given the way in which it tends

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to think about causal relationships in a very narrow and,

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and myopic manner.

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Uh, so to help with that, to compliment that approach,

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and to, to expand it, I suggest that we should,

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should still

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Use those techniques,

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but use them alongside the techniques that we have,

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with the other two approaches at the same time.

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And if we do this well as social scientists

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and as management researchers, so that we,

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in a programmatic manner, systematically

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use them alongside one another for particular topic areas,

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particular phenomena that we're studying, I have no doubt

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that will come up with stronger causal in infants, that

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that will lead to better explanations,

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and as I said, will also inform policy and practice.

00:00 - Causal relationships, have been a concern within management research and across the social sciences for, for a long time. and of causal relationship is really the idea that for the events are happening in the world that we study, we can really say that one, condition A is seen

00:16 - to produce a particular effect, b and that seemed to be important to be able to establish those causal relationships so that in no uncertain terms and beyond basic correlations, we can say a will on in general lead, lead to B, either in a very direct deterministic sense that A, always B always follows from a, the one condition, or the one effect from, the base condition, or maybe in a more probabilistic sense

00:46 - that a on average is more likely to, to lead to B. So this has been a ma a main preoccupation of, many social scientists, including management research for a very long time. Also, with the perspective that if we, if we're able to properly identify these causal relationships, we can also more effectively inform policy and practice on the things that, managers or policy makers might, might do. Yeah. What is interesting is that the topic of causation,

01:16 - and also questions around causal relationships, and also the idea that we need to be really certain, be able to make a robust inference as to whether a causal relationship is there. These types of questions haven't really been su substantively discussed, to any great extent. So most of the discussion that has taken place is really at the level of methods where, typically by importing, techniques and methods and research designs from econometrics

01:45 - or from sociology, people have made assumptions about what causal relationships are and how they can following these methods, be properly identified in each of these different contexts. But what is important to remember here is that, if you compare, let's say, a experimental design with a cross case comparative analysis method, the assumptions that are being made about what causation is, what a causal relationship is, and the criteria that are also coming with these methods as

02:16 - to how to make a causal inference, how to make an inference that a causal relationship is there, are actually very different. and that discussion hasn't really taken place yet. And I think it's important to first figure that out how we generally think about causal relationships, alongside applying some of these methodological techniques. yeah, that's, it is a very good question. So in the main, I've identified that there are three ways in which we tend to approach

02:47 - questions of causation and tend to approach the, the issue of how to identify cause relationships. So there's a propositional approach, which is basically what it says where researchers Hypothesized that, two events or, happenings are related as a simple cause effect scenario. So one condition, A, directly effects B, either in a direct way or through some mediating path.

03:12 - So through c having that ultimate effect, on B. So you have these very simple if done scenarios where researchers subsequently do their best through their research designs and through their, intervention techniques to make sure that in a very controlled manner, they can actually establish that b in no uncertain terms follows from, from a, so that's the propositional approach. Then we have the configurational approach,

03:40 - which actually emerged as, an antidote or a challenge to the propositional approach, where the propositional one is really looking at a root cause or root condition, or presupposes a root cause or root condition, and then looks at the subsequent effect In a very simple sort of causal scenario, the configurational approach says, well, actually, what might be better given that many phenomena are very complex, is to look at a particular outcome or effect that has materialized

04:10 - and then look backwards at all the sets of conditions and processes along, across those conditions that might have brought that particular effect about. So it's a very different approach where instead of looking at the effects of presupposed causes, you look at what might be the potential causes or the potential conditions that might have caused, a particular effect. And it's as an approach also, compared to the propositional one, much more alive to

04:40 - a complex causal field, a whole range of conditions as potential causes that might have brought the effect about. So your frame of reference, is enlarged, but you also with that look at multiple conditions alongside one another, and you try and figure out through your reasoning and through your probing of what you found. so typically these, this is post case comparisons that you, or,

05:07 - or comparative research design that you use across cases or across data sets where you try and not just identify these potential conditions, but also counterfactually probe, which of the ones or which of the sets of conditions alone or together might have brought this effect about. So it's a much more systemic form of causation compared to the, the propositional approach. And then finally, the process approach, is, is again, another approach.

05:36 - So it says, instead of listing sets of conditions on the one hand and effects on the other, either in a very simple way with the propositional approach or in a more complex way with a configurational approach, what we should be doing instead is to follow in a more longitudinal sense how hap how events play out over time so that we can really see what is the sequencing of events that might have materialized in a particular case

06:06 - or cross cases, and maybe also across larger data sets, if we have that longitudinal, frame of reference for those data to see what effect materialized in that particular case. So it's a, an approach that takes temporality and takes the longitudinal perspective very seriously, and is also alive to the very uncertain probabilistic nature of the world where sometimes effects get brought about through certain sequences of events that, that happen. So they have their own, that approach comes

06:40 - with its own methodology, it's longitudinal, but there's also various techniques that you can use to, to bracket sequences of events to see the transition points between these, these sequences and to try and decipher a particular causal process running, alongside that trajectory. Yeah, I think, I think the most interesting thing is that, so each of these approach approaches obviously comes with its own, prepositions of how to identify causal relationships.

07:11 - And it also makes very different assumptions about where we find these causal relationships and where and how causation happens. And one, obvious observation is, and there's a philosopher called Nancy Cartwright, who among many others has, has spotted this observation, is that some approaches are really looking at, so she calls that the really, really, using causes or manipulating causes. So think of the propositional approach where you say, okay,

07:40 - I'm assuming this is a, a root cause or a true cause that will bring a certain effect about either in a deterministic or in a more probabilistic sense. Then I'm trying to find research designs and data sets that allow me to manipulate or in a very controlled manner, see whether that's the case. And then, so it's really the look looking at the effects of presumed cause then the other approaches, so the process and configurational are actually the opposite, so it's more hunting for causes.

08:13 - So I know that these types of effects as phenomena generally occur in the world. Let's look back and see what might have been, all the sets of conditions and, and potential causes, in those terms that I brought it about. So it's more hunting for causes, trying to identify and, and weed out the sets of conditions, that may have, may have not mattered too much. Then the sets of conditions that we now can say

08:45 - are actual causes that produce this, this outcome. So it's, it's as an approach compared to the using or manipulating causes and looking at these very controlled intervention, experimental based designs that match that. It's as an approach taking a very different, stance and looking more at the world out there, so phenomena that can be observed effects or outcomes that can be observed and then be reasoning backwards.

09:13 - So I think that's a very fundamental distinction, and it's actually very helpful to think about it because it also allows you to say where the one comes with a blind spot. You know, if I only reason forward and presume that condition A will BA root cause and will in most instances lead to B, well, maybe the reality is a bit more complex and we have something to learn from the other approach. not to say that, you know, I'm not trying

09:40 - to favor the one over the other, but, the point with the triangulation idea is to say that they all come with different vantage points. They all come with different strengths and limitations and also different criteria for making, an influence around the causal relationship. So let's join them up, let's see what they collectively offer so that we get a more robust influence as to whether causal relationship is there, and can

10:05 - accordingly theorize better come up with stronger explanations, but also inform policy and practice in a, in a much more effective way. and to back that up, in the paper, I show how some of the classic criteria for how we think about, for how we think about a cause of relationship, so what a cause of relationship needs to have in order for us to talk about not just the correlation, but a course of relationship, how these criteria are not satisfied by one approach,

10:37 - but they're more likely to be satisfied by multiple approaches. So the propositional one is very good with its controlled intervention methodology to try and identify whether a course temporally precedes an effect. So the course or precedence that is a classic criteria of, of acausal relationship is, is something that that approach can do really well, but it also has a lot of limitations. I already noted the lack of its ability to deal

11:06 - with complexity, the lack of its ability to deal with spurious causation. The lack of its ability to really beyond doubt show that there is a dependent relationship between A and B. So B will necessary and sufficiently follow from, from a, those are all things that it struggles with. Whereas the other approaches then have, have equity strengths and limitations. So the process one is very good at, as I described it,

11:34 - to trace the, the real transitive causal process that goes through the sets of conditions, or the events that you've, you've categorized as conditions or potential causes over time. So tracing that through. So the classic notion that we can speak of causation when A, produce an, an effect, and B, b produce an effect C, you can trace that or be much closer to that with the process approach than, with the other two approaches.

12:05 - But equally, the process approach also has its limitations, and again, causal dependence to really showing that b depends on a, is very difficult to establish in and of itself by that approach. And then the configurational approach is very good in trying to force yourself to look at the world out there, not limit yourself to one presupposed condition or limited set of conditions as potential causes, but really look at the world out there. What are the regularly occurring connections

12:38 - between potential causes, conditions as causes, and the effects that that materialize. So it has this classic criteria of regularity, in, in the events that we observe and the connections between them, which is, which has for a long time been suggested to be one of the key criteria of a, of a course of relationship. it does that really well. but for its bite, it also has its own limitations because it is, it gives you those regularly,

13:08 - occurring connections, but then again, it, it struggles to deal with cause of dependence. There may be spurious correlations in there. And, and this is again, where the other approaches in combination can compliment and offset some of the limitations, that each of them have. I think the, the future, so the, the future implication of this is really around,

13:32 - methodological practice, so that as social scientists and as management researchers, we should really embrace this idea that if we want to make robust calls or in infants, there's not one single route is not what single approach to cause and identification, but we really benefit when we, in our, inquiries into particular phenomena make the most of these different approaches together and use them alongside each other in ways

14:00 - that offset the biases and limitations of the one approach, but also in, in simultaneously complement each other in, in moving towards these more robust causal in infants. so that is the idea with causal triangulation, where previously we talked a lot about causal identification, and that's an approach that primarily comes out of econometrics and is a set of techniques that fit with the propositional approach to theorizing, that has its, its its value and its advantages,

14:30 - but as I've explained, is still, limited given the way in which it tends to think about causal relationships in a very narrow and, and myopic manner. so to help with that, to compliment that approach, and to, to expand it, I suggest that we should, should still Use those techniques, but use them alongside the techniques that we have, with the other two approaches at the same time.

14:57 - And if we do this well as social scientists and as management researchers, so that we, in a programmatic manner, systematically use them alongside one another for particular topic areas, particular phenomena that we're studying, I have no doubt that will come up with stronger causal in infants, that that will lead to better explanations, and as I said, will also inform policy and practice.

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