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.

Graphs and Occam’s Razor

One argument / point of criticism I often hear from people who start exploring Directed Acyclic Graphs (DAG) is that graphical models can quickly become very complex. When you read about the methodology for the first time you get walked through all these toy models – small, well-behaved examples with nice properties, in which causal inference works like a charm.

Continue reading Graphs and Occam’s Razor

Sample Selection Vs. Selection Into Treatment

This is an issue that bothered me for quite some time. So I finally decided to settle it with a blog post. I see people constantly confusing the two most common threats to causal inference—sample selection and endogeneity. This happens, for example, quite often in management research, where it is common to recommend a sample selection model in order to deal with endogenous treatments. But the two concepts are far from being equivalent. Have a look at the following graph, which describes a typical case of endogeneity. Continue reading Sample Selection Vs. Selection Into Treatment

Why you shouldn’t control for post-treatment variables in your regression

This is a slight variation of a theme, I was already blogging about some time ago. But I recently had a discussion with a colleague and thought it would be worthwhile to share my notes here. So what might go wrong if you control for post-treatment variables in your statistical model? Continue reading Why you shouldn’t control for post-treatment variables in your regression

Microsoft Releases New Python Library for Causal Inference

A while ago I blogged about Facebook’s causal inference group. Now Microsoft has followed suit and released a Python library for graph-based methods of causal inference. Continue reading Microsoft Releases New Python Library for Causal Inference

The Origins of Graphical Causal Models

Here is an interesting bit of intellectual history. In his 2000 book “Causality”, Judea Pearl describes how he got to the initial idea that sparked the development of causal inference based on directed acyclic graphs. Continue reading The Origins of Graphical Causal Models