# Feature selection in stratification estimators of causal effects: lessons from potential outcomes, causal diagrams, and structural equations

arXiv: https://arxiv.org/abs/2209.11400 · HTML: https://mlsanity.com/paper/2209.11400/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 42 references that are in the map.
- First posted on arXiv 23 Sep 2022.

## Builds on (most cited first, 2 of 2)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [A Bayesian Solution to the M-Bias Problem](https://mlsanity.com/paper/1906.07136/) (2019)

## Built on by (most cited first, 2 of 2)

- [Local Discovery by Partitioning: Polynomial-Time Causal Discovery Around Exposure-Outcome Pairs](https://mlsanity.com/paper/2310.17816/) (2023)
- [New User Event Prediction Through the Lens of Causal Inference](https://mlsanity.com/paper/2407.05625/) (2024)

## Abstract (arXiv)

What is the ideal regression (if any) for estimating average causal effects? We study this question in the setting of discrete covariates, deriving expressions for the finite-sample variance of various stratification estimators. This approach clarifies the fundamental statistical phenomena underlying many widely-cited results. Our exposition combines insights from three distinct methodological traditions for studying causal effect estimation: potential outcomes, causal diagrams, and structural models with additive errors.