Online Program Home
My Program

Abstract Details

Activity Number: 145 - Causal Inference
Type: Invited
Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
Sponsor: IMS
Abstract #300097
Title: Anchor Regression: Heterogeneous Data Meets Causality
Author(s): Dominik Rothenhäusler* and Nicolai Meinshausen and Peter Bühlmann and Jonas Peters
Companies: UC Berkeley and ETH Zürich and ETH Zurich and University of Copenhagen
Keywords: Heterogeneous data; Instrumental variables approach; Predictive stability

Many traditional statistical prediction methods mainly deal with the problem of overfitting to the given data set. On the other hand, there is a vast literature on the estimation of causal parameters for prediction under interventions. However, both types of estimators can perform poorly when used for prediction on heterogeneous data. We discuss the delicate trade-off between predictive performance on the training data and perturbed data. In particular, under a linear structural equation model with exogenous variables, we show that the change in loss under certain perturbations (interventions) can be written as a convex penalty. This motivates anchor regression, a regularization scheme that encourages the estimator to generalize well to perturbed data. Under instrumental variable (IV) assumptions, the procedure naturally provides an interpolation between the solution to ordinary least squares and the IV estimator. The proposed procedure allows statisticians and practitioners to trade-off predictive performance on the distribution of the training data and on distributions which are perturbed versions of what is seen in the training data.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2019 program