Causal theories described in Pearl (2009) seemingly find more and more attention in methodological papers (Elwert and Winship, 2014; Pearl, Glymour and Jewell, 2016; Lewbel, 2019; Imbens, 2019).

But are there any good, practical applications of Pearl's causal theories in empirical research? Have you encountered such papers?

Elwert, Felix, and Christopher Winship. "Endogenous selection bias: The problem of conditioning on a collider variable." Annual review of sociology 40 (2014): 31-53.

Imbens, Guido. Potential outcome and directed acyclic graph approaches to causality: Relevance for empirical practice in economics. No. w26104. National Bureau of Economic Research, 2019.

Lewbel, Arthur. "The identification zoo: Meanings of identification in econometrics." Journal of Economic Literature 57, no. 4 (2019): 835-903.

Pearl, Judea. Causality. Cambridge university press, 2009.

Pearl, Judea, Madelyn Glymour, and Nicholas P. Jewell. Causal inference in statistics: A primer. John Wiley & Sons, 2016.

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    $\begingroup$ canr.msu.edu/afre/events/Bellemare%20and%20Bloem%20(2020).pdf $\endgroup$
    – dimitriy
    Commented Dec 6, 2020 at 1:38
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    $\begingroup$ +1 this relates to my comments to this question $\endgroup$ Commented Dec 6, 2020 at 10:48
  • $\begingroup$ @DimitriyV.Masterov One odd thing about that paper is that they choose to use the notation $E[Y|X]$ to define the causal parameter of interest and then later they start introducing the $P(Y|do(X))$ notation reminding the reader $P(Y|X)$ needn't be causal $\endgroup$
    – CloseToC
    Commented Dec 6, 2020 at 11:02

1 Answer 1


The question is: "Are there any good, practical applications of Pearl's causal theories in empirical research?", and my answer is yes, likely occurring, however, as a prerequisite or possibly embedded.

First, some background, courtesy of a paper provided by Judea Pearl and T.S. Verma: A Theory of Inferred Causation. Per the reference, an analysis of causation can encompass the identification of possible explanatory 'clues' and also inferring causative models from said clues.

Then, there is, for example, this work: Causality in Decision Making by York Hagmayer and Philip M. Fernbach, to quote:

To summarize, causal decision theory argues that knowledge of the statistical relationships between an action and a desired outcome is sometimes insufficient for making the best choice. Based on assumptions about the underlying causal structure causal expected utilities can be calculated which allow the decision maker to identify and choose the action....reasonable utilities can only be generated if one has knowledge of the causal structure relating the chosen action and the desired outcome. Thus, causal knowledge is central to rational decision-making.

The last sentence makes my point, there are many articles, not just this cited example, which rest on a premise of causal knowledge/structure for which the work of Pearl can provide a cornerstone.

Other related examples of possible applications include Mixed Causal Structure Discovery with Application to Prescriptive Pricing and A causal inference approach to measure price elasticity in Automobile Insurance.


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