# Identification and Estimation of Long-Term Treatment Effects with Monotone Missing

arXiv: https://arxiv.org/abs/2504.19527 · HTML: https://mlsanity.com/paper/2504.19527/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 54 references that are in the map.
- First posted on arXiv 28 Apr 2025.

## Builds on (most cited first, 11 of 11)

- [On the role of surrogates in the efficient estimation of treatment effects with limited outcome data](https://mlsanity.com/paper/2003.12408/) (2020)
- [Long-term Causal Inference Under Persistent Confounding via Data Combination](https://mlsanity.com/paper/2202.07234/) (2022)
- [Targeting for long-term outcomes](https://mlsanity.com/paper/2010.15835/) (2020)
- [Estimating Individual Treatment Effects using Non-Parametric Regression Models: a Review](https://mlsanity.com/paper/2009.06472/) (2020)
- [Long-Term Effect Estimation with Surrogate Representation](https://mlsanity.com/paper/2008.08236/) (2020)
- [Long-term Causal Effects Estimation via Latent Surrogates Representation Learning](https://mlsanity.com/paper/2208.04589/) (2022)
- [Estimating long-term causal effects from short-term experiments and long-term observational data with unobserved confounding](https://mlsanity.com/paper/2302.10625/) (2023)
- [Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments](https://mlsanity.com/paper/2311.08527/) (2023)
- [Identification and estimation of treatment effects on long-term outcomes in clinical trials with external observational data](https://mlsanity.com/paper/2208.10163/) (2022)
- [Policy Learning for Balancing Short-Term and Long-Term Rewards](https://mlsanity.com/paper/2405.03329/) (2024)
- [Covariate adjustment in randomized experiments with missing outcomes and covariates](https://mlsanity.com/paper/2311.10877/) (2023)

## Abstract (arXiv)

Estimating long-term treatment effects has a wide range of applications in various domains. A key feature in this context is that collecting long-term outcomes typically involves a multi-stage process and is subject to monotone missing, where individuals missing at an earlier stage remain missing at subsequent stages. Despite its prevalence, monotone missing has been rarely explored in previous studies on estimating long-term treatment effects. In this paper, we address this gap by introducing the sequential missingness assumption for identification. We propose three novel estimation methods, including inverse probability weighting, sequential regression imputation, and sequential marginal structural model (SeqMSM). Considering that the SeqMSM method may suffer from high variance due to severe data sparsity caused by monotone missing, we further propose a novel balancing-enhanced approach, BalanceNet, to improve the stability and accuracy of the estimation methods. Extensive experiments on two widely used benchmark datasets demonstrate the effectiveness of our proposed methods.