# Accounting for Missing Covariates in Heterogeneous Treatment Estimation

arXiv: https://arxiv.org/abs/2410.15655 · HTML: https://mlsanity.com/paper/2410.15655/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 40 references that are in the map.
- First posted on arXiv 21 Oct 2024.

## Builds on (most cited first, 18 of 18)

- [Semiparametric doubly robust targeted double machine learning: a review](https://mlsanity.com/paper/2203.06469/) (2022)
- [Removing Hidden Confounding by Experimental Grounding](https://mlsanity.com/paper/1810.11646/) (2018)
- [Causal inference methods for combining randomized trials and observational studies: a review](https://mlsanity.com/paper/2011.08047/) (2020)
- [Learning Optimized Risk Scores](https://mlsanity.com/paper/1610.00168/) (2016)
- [Semiparametric counterfactual density estimation](https://mlsanity.com/paper/2102.12034/) (2021)
- [Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects](https://mlsanity.com/paper/2202.12891/) (2022)
- [Sharp instruments for classifying compliers and generalizing causal effects](https://mlsanity.com/paper/1801.03635/) (2018)
- [Prediction-powered Generalization of Causal Inferences](https://mlsanity.com/paper/2406.02873/) (2024)
- [Multi-Source Causal Inference Using Control Variates](https://mlsanity.com/paper/2103.16689/) (2021)
- [Meta-Learners for Partially-Identified Treatment Effects Across Multiple Environments](https://mlsanity.com/paper/2406.02464/) (2024)
- [Data fusion methods for the heterogeneity of treatment effect and confounding function](https://mlsanity.com/paper/2007.12922/) (2020)
- [Nuances in Margin Conditions Determine Gains in Active Learning](https://mlsanity.com/paper/2110.08418/) (2021)
- [To Impute or not to Impute? Missing Data in Treatment Effect Estimation](https://mlsanity.com/paper/2202.02096/) (2022)
- [Minimax Rates and Adaptivity in Combining Experimental and Observational Data](https://mlsanity.com/paper/2109.10522/) (2021)
- [Doubly robust estimators for generalizing treatment effects on survival outcomes from randomized controlled trials to a target population](https://mlsanity.com/paper/2201.06595/) (2022)
- [Auditing Fairness under Unobserved Confounding](https://mlsanity.com/paper/2403.14713/) (2024)
- [Sensitivity analysis for an unobserved moderator in RCT-to-target-population generalization of treatment effects](https://mlsanity.com/paper/1610.06943/) (2016)
- [Multiclass learning with margin: exponential rates with no bias-variance trade-off](https://mlsanity.com/paper/2202.01773/) (2022)

## Abstract (arXiv)

Many applications of causal inference require using treatment effects estimated on a study population to make decisions in a separate target population. We consider the challenging setting where there are covariates that are observed in the target population that were not seen in the original study. Our goal is to estimate the tightest possible bounds on heterogeneous treatment effects conditioned on such newly observed covariates. We introduce a novel partial identification strategy based on ideas from ecological inference; the main idea is that estimates of conditional treatment effects for the full covariate set must marginalize correctly when restricted to only the covariates observed in both populations. Furthermore, we introduce a bias-corrected estimator for these bounds and prove that it enjoys fast convergence rates and statistical guarantees (e.g., asymptotic normality). Experimental results on both real and synthetic data demonstrate that our framework can produce bounds that are much tighter than would otherwise be possible.