# Variable Selection in Causal Inference using a Simultaneous Penalization Method

arXiv: https://arxiv.org/abs/1511.08501 · HTML: https://mlsanity.com/paper/1511.08501/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2015.

## Built on by (most cited first, 3 of 3)

- [Bayesian Model Averaging for Data Driven Decision Making when Causality is Partially Known](https://mlsanity.com/paper/2105.05395/) (2021)
- [A Two-Stage Feature Selection Approach for Robust Evaluation of Treatment Effects in High-Dimensional Observational Data](https://mlsanity.com/paper/2111.13800/) (2021)
- [A Two-Stage Interpretable Matching Framework for Causal Inference](https://mlsanity.com/paper/2504.09635/) (2025)

## Abstract (arXiv)

In the causal adjustment setting, variable selection techniques based on one of either the outcome or treatment allocation model can result in the omission of confounders, which leads to bias, or the inclusion of spurious variables, which leads to variance inflation, in the propensity score. We propose a variable selection method based on a penalized objective function which considers the outcome and treatment assignment models simultaneously. The proposed method facilitates confounder selection in high-dimensional settings. We show that under regularity conditions our method attains the oracle property. The selected variables are used to form a doubly robust regression estimator of the treatment effect. We show that under some conditions our method attains the oracle property. Simulation results are presented and economic growth data are analyzed. Specifically, we study the effect of life expectancy as a measure of population health on the average growth rate of gross domestic product per capita.