"Robust Causal Inference with Continuous Exposures" (This Week at the Statistics Seminar)

Attention conservation notice: Only of interest if you (1) care about statistical methods for causal inference, and (2) will be in Pittsburgh on Thursday.

There are whole books on causal inference which make it seem like the subject is exhausted by comparing the effect of The Treatment to the control condition. (cough Imbens and Rubin cough) But any approach to causal inference which can't grasp a dose-response curve might be sound but is not complete. Nor is there any reason, in this day and age, to stick to simple regression. Fortunately, we don't have to:

Edward Kennedy, "Robust Causal Inference with Continuous Exposures" (arxiv:1507.00747)

Abstract: Continuous treatments (e.g., doses) arise often in practice, but standard causal effect estimators are limited: they either employ parametric models for the effect curve, or else do not allow for doubly robust covariate adjustment. Double robustness allows one of two nuisance estimators to be misspecified, and is important for protecting against model misspecification as well as reducing sensitivity to the curse of dimensionality. In this work we develop a novel approach for causal dose-response curve estimation that is doubly robust without requiring any parametric assumptions, and which naturally incorporates general off-the-shelf machine learning. We derive asymptotic properties for a kernel-based version of our approach and propose a method for data-driven bandwidth selection. The methods are illustrated via simulation and in a study of the effect of hospital nurse staffing on excess readmissions penalties.

Time and place: 4:30--5:30 pm on Thursday, 18 February 2016, in Baker Hall A51

As always, the talk is free and open to the public.

Enigmas of Chance; Constant Conjunction Necessary Connexion