Author: Susan Athey
A large literature on causal inference in statistics, econometrics, biostatistics, and epidemiology (see, e.g., Imbens and Rubin  for a recent survey) has focused on methods for statistical estimation and inference in a setting where the researcher wishes to answer a question about the (counterfactual) impact of a change in a policy, or “”treatment”” in the terminology of the literature. The policy change has not necessarily been observed before, or may have been observed only for a subset of the population; examples include a change in minimum wage law or a change in a firm’s price. The goal is then to estimate the impact of small set of “”treatments”” using data from randomized experiments or, more commonly, “”observational”” studies (that is, non-experimental data). The literature identifies a variety of assumptions that, when satisfied, allow the researcher to draw the same types of conclusions that would be available from a randomized experiment. To estimate causal effects given non-random assignment of individuals to alternative policies in observational studies, popular techniques include propensity score weighting, matching, and regression analysis; all of these methods adjust for differences in observed attributes of individuals. Another strand of literature in econometrics, referred to as “”structural modeling,”” fully specifies the preferences of actors as well as a behavioral model, and estimates those parameters from data (for applications to auction-based electronic commerce, see Athey and Haile  and Athey and Nekipelov ). In both cases, parameter estimates are interpreted as “”causal,”” and they are used to make predictions about the effect of policy changes. In contrast, the supervised machine learning literature has traditionally focused on prediction, providing data-driven approaches to building rich models and relying on cross-validation as a powerful tool for model selection. These methods have been highly successful in practice. This talk will review several recent papers that attempt to bring the tools of supervised machine learning to bear on the problem of policy evaluation, where the papers are connected by three themes.
The first theme is that it important for both estimation and inference to distinguish between parts of the model that relate to the causal question of interest, and “”attributes,”” that is, features or variables that describe attributes of individual units that are held fixed when policies change. Specifically, we propose to divide the features of a model into causal features, whose values may be manipulated in a counterfactual policy environment, and attributes. A second theme is that relative to conventional tools from the policy evaluation literature, tools from supervised machine learning can be particularly effective at modeling the association of outcomes with attributes, as well as in modeling how causal effects vary with attributes. A final theme is that modifications of existing methods may be required to deal with the “”fundamental problem of causal inference,”” namely, that no unit is observed in multiple counterfactual worlds at the same time: we do not see a patient at the same time with and without medication, and we do not see a consumer at the same moment exposed to two different prices. This creates a substantial challenge for cross-validation, as the ground truth for the causal effect is not observed for any individual.