Causal Inference for Policymaking

I just submitted an extended abstract of an upcoming paper to a conference that will discuss new analytical tools and techniques for policymaking. The abstract contains a brief discussion about the importance of causal inference for taking informed policy decisions. And I would like to share these thoughts here.

Causal inference lies at the heart of policy-making, since every policy measure aims at actively manipulating certain economic variables in order to achieve a desired goal. To make an informed decision about which measures to implement, policy makers need to have knowledge about the likely impact of their actions. Newly emerging approaches in machine learning and predictive analytics are inherently inadequate to supply this kind of knowledge though, as they remain purely correlation-based and are thus not able to address causal questions.

Based on the seminal work by Judea Pearl (2000), the literature on causal inference in computer science and artificial intelligence (AI) has developed unique tools to tackle causal prediction problems, which go well beyond the standard approaches in econometrics. Areas in which this literature has made important contributions are as diverse as:

  1. Estimating causal effects with observational data
  2. Learning from surrogate experiments (“encouragement designs”)
  3. Dealing with selection bias
  4. External validity of policy experiments
  5. Transporting experimental results across heterogeneous populations

This paper synthesizes recent advances in the field of causal AI and gives an overview of how these techniques add to the existing econometric toolbox. We show how—in particular combined with the large data sets that are increasingly becoming available—these approaches provide entirely new avenues for policy research. Since other disciplines, such as epidemiology, sociology, and political science, were much quicker than economics in adopting these tools, our hope is that our paper will contribute to a catching up in this direction.

Pearl, J. (2000): Causality: Models, Reasoning, and Inference, New York, United States, NY: Cambridge University Press.

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