We define the notion of a minimal control function, on the basis of which a novel regression penalty is devised that is unbiased for average treatment effects. The development of the new approach combines insights from three distinct methodological traditions for studying causal effect estimation: potential outcomes, causal diagrams, and structural models with additive errors. It is demonstrated that traditional feature selection and/or regularization approaches to treatment effect estimation can exhibit severe bias for average and conditional average treatment effects. This is joint work with Drew Herren.

**DoMSS Seminar
Monday, January 24
1:30 pm
Zoom meeting room link: https://asu.zoom.us/j/6871076660**

*Note: This meeting will be via Zoom. This semester, we anticipate some talks will be in person but most will be by Zoom.*

Richard Hahn

Associate Professor of Statistics

Arizona State University