# Inferring the Long-Term Causal Effects of Long-Term Treatments from Short-Term Experiments

arXiv: https://arxiv.org/abs/2311.08527 · HTML: https://mlsanity.com/paper/2311.08527/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2023.

## Built on by (most cited first, 9 of 9)

- [Predictions as Surrogates: Revisiting Surrogate Outcomes in the Age of AI](https://mlsanity.com/paper/2501.09731/) (2025)
- [Estimating Long-term Heterogeneous Dose-response Curve: Generalization Bound Leveraging Optimal Transport Weights](https://mlsanity.com/paper/2406.19195/) (2024)
- [Nonparametric Heterogeneous Long-term Causal Effect Estimation via Data Combination](https://mlsanity.com/paper/2502.18960/) (2025)
- [Long-term Causal Inference via Modeling Sequential Latent Confounding](https://mlsanity.com/paper/2502.18994/) (2025)
- [Estimate Level Adjustment For Inference With Proxies Under Random Distribution Shifts](https://mlsanity.com/paper/2605.06484/) (2026)
- [Short-Long Policy Evaluation with Novel Actions](https://mlsanity.com/paper/2407.03674/) (2024)
- [Predicting Long Term Sequential Policy Value Using Softer Surrogates](https://mlsanity.com/paper/2412.20638/) (2024)
- [Identification and Estimation of Long-Term Treatment Effects with Monotone Missing](https://mlsanity.com/paper/2504.19527/) (2025)
- [Sequential Treatment Effect Estimation with Unmeasured Confounders](https://mlsanity.com/paper/2505.09113/) (2025)

## Abstract (arXiv)

We study inference on the long-term causal effect of a continual exposure to a novel intervention, which we term a long-term treatment, based on an experiment involving only short-term observations. Key examples include the long-term health effects of regularly-taken medicine or of environmental hazards and the long-term effects on users of changes to an online platform. This stands in contrast to short-term treatments or "shocks," whose long-term effect can reasonably be mediated by short-term observations, enabling the use of surrogate methods. Long-term treatments by definition have direct effects on long-term outcomes via continual exposure, so surrogacy conditions cannot reasonably hold. We connect the problem with offline reinforcement learning, leveraging doubly-robust estimators to estimate long-term causal effects for long-term treatments and construct confidence intervals.