Colloque des sciences mathématiques du Québec

24 septembre 2021 de 15 h 00 à 16 h 00 (heure de Montréal/HNE)

Deep down, everyone wants to be causal

Colloque par Jennifer Hill (NYU Steinhardt)

Most researchers in the social, behavioral, and health sciences are taught to be extremely cautious in making causal claims.  However, causal inference is a necessary goal in research for addressing many of the most pressing questions around policy and practice.  In the past decade, causal methodologists have increasingly been using and touting the benefits of more complicated machine learning algorithms to estimate causal effects.  These methods can take some of the guesswork out of analyses, decrease the opportunity for “p-hacking,” and may be better suited for more fine-tuned tasks such as identifying varying treatment effects and generalizing results from one population to another.  However, should these more advanced methods change our fundamental views about how difficult it is to infer causality? In this talk I will discuss some potential advantages and disadvantages of using machine learning for causal inference and emphasize ways that we can all be more transparent in our inferences and honest about their limitations.