# Causal Inference: A Missing Data Perspective

arXiv: https://arxiv.org/abs/1712.06170 · HTML: https://mlsanity.com/paper/1712.06170/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2017.

## Built on by (most cited first, 15 of 15)

- [Applied Causal Inference Powered by ML and AI](https://mlsanity.com/paper/2403.02467/) (2024)
- [Learning from data with structured missingness](https://mlsanity.com/paper/2304.01429/) (2023)
- [DoubleGen: Debiased Generative Modeling of Counterfactuals](https://mlsanity.com/paper/2509.16842/) (2025)
- [Estimating Potential Outcome Distributions with Collaborating Causal Networks](https://mlsanity.com/paper/2110.01664/) (2021)
- [Efficient Heterogeneous Treatment Effect Estimation With Multiple Experiments and Multiple Outcomes](https://mlsanity.com/paper/2206.04907/) (2022)
- [Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?](https://mlsanity.com/paper/2304.11749/) (2023)
- [Bayesian Safe Policy Learning with Chance Constrained Optimization: Application to Military Security Assessment during the Vietnam War](https://mlsanity.com/paper/2307.08840/) (2023)
- [Calibeating Prediction-Powered Inference](https://mlsanity.com/paper/2604.21260/) (2026)
- [Efficient Learning of Optimal Individualized Treatment Rules for Heteroscedastic or Misspecified Treatment-Free Effect Models](https://mlsanity.com/paper/2109.02570/) (2021)
- [Counterfactually Comparing Abstaining Classifiers](https://mlsanity.com/paper/2305.10564/) (2023)
- [Estimating Gaussian Copulas with Missing Data](https://mlsanity.com/paper/2201.05565/) (2022)
- [Retrospective Counterfactual Prediction by Conditioning on the Factual Outcome: A Cross-World Approach](https://mlsanity.com/paper/2603.27320/) (2026)
- [Structure Maintained Representation Learning Neural Network for Causal Inference](https://mlsanity.com/paper/2508.01865/) (2025)
- [Causal Discovery Should Embrace the Wisdom of the Crowd](https://mlsanity.com/paper/2603.02678/) (2026)
- [Causal-Audit: A Framework for Risk Assessment of Assumption Violations in Time-Series Causal Discovery](https://mlsanity.com/paper/2604.02488/) (2026)

## Abstract (arXiv)

Inferring causal effects of treatments is a central goal in many disciplines. The potential outcomes framework is a main statistical approach to causal inference, in which a causal effect is defined as a comparison of the potential outcomes of the same units under different treatment conditions. Because for each unit at most one of the potential outcomes is observed and the rest are missing, causal inference is inherently a missing data problem. Indeed, there is a close analogy in the terminology and the inferential framework between causal inference and missing data. Despite the intrinsic connection between the two subjects, statistical analyses of causal inference and missing data also have marked differences in aims, settings and methods. This article provides a systematic review of causal inference from the missing data perspective. Focusing on ignorable treatment assignment mechanisms, we discuss a wide range of causal inference methods that have analogues in missing data analysis, such as imputation, inverse probability weighting and doubly-robust methods. Under each of the three modes of inference--Frequentist, Bayesian, and Fisherian randomization--we present the general structure of inference for both finite-sample and super-population estimands, and illustrate via specific examples. We identify open questions to motivate more research to bridge the two fields.