Title

On Estimating Multiple Treatment Effects with Regression

Author(s)

Paul Goldsmith-Pinkham Paul Goldsmith-Pinkham (Yale University)

Peter Hull Peter Hull (University of Chicago)

Michal Kolesár Michal Kolesár (Princeton University)

Abstract

We study the causal interpretation of regressions on multiple dependent treatments and flexible controls. Such regressions are often used to analyze randomized control trials with multiple intervention arms, and to estimate institutional quality (e.g. teacher value-added) with observational data. We show that, unlike with a single binary treatment, these regressions do not generally estimate convex averages of causal effects—even when the treatments are conditionally randomly assigned and the controls fully address omitted variables bias. We discuss different solutions to this issue, and propose as a solution a new class of efficient estimators of weighted average treatment effects.

Creation Date

2021-06

Section URL ID

Paper Number

2021-41

URL

https://arxiv.org/pdf/2106.05024.pdf

File Function

Jel

C30

Keyword(s)

regressions, treatment effect

Suppress

false

Series

13