# Continuous Treatment Effects with Surrogate Outcomes

arXiv: https://arxiv.org/abs/2402.00168 · HTML: https://mlsanity.com/paper/2402.00168/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 57 references that are in the map.
- First posted on arXiv 31 Jan 2024.

## Builds on (most cited first, 15 of 15)

- [Semiparametric doubly robust targeted double machine learning: a review](https://mlsanity.com/paper/2203.06469/) (2022)
- [Nonparametric methods for doubly robust estimation of continuous treatment effects](https://mlsanity.com/paper/1507.00747/) (2015)
- [On the role of surrogates in the efficient estimation of treatment effects with limited outcome data](https://mlsanity.com/paper/2003.12408/) (2020)
- [Generalizing causal inferences from individuals in randomized trials to all trial-eligible individuals](https://mlsanity.com/paper/1709.04589/) (2017)
- [Sharp instruments for classifying compliers and generalizing causal effects](https://mlsanity.com/paper/1801.03635/) (2018)
- [Covariate-assisted bounds on causal effects with instrumental variables](https://mlsanity.com/paper/2301.12106/) (2023)
- [A General Framework for Treatment Effect Estimation in Semi-Supervised and High Dimensional Settings](https://mlsanity.com/paper/2201.00468/) (2022)
- [Fast convergence rates for dose-response estimation](https://mlsanity.com/paper/2207.11825/) (2022)
- [Estimating scaled treatment effects with multiple outcomes](https://mlsanity.com/paper/1608.02273/) (2016)
- [Debiased inference for a covariate-adjusted regression function](https://mlsanity.com/paper/2210.06448/) (2022)
- [Nested Nonparametric Instrumental Variable Regression](https://mlsanity.com/paper/2112.14249/) (2021)
- [Kernel methods for long term dose response curves](https://mlsanity.com/paper/2201.05139/) (2022)
- [Efficient Generalization and Transportation](https://mlsanity.com/paper/2302.00092/) (2023)
- [Flexibly Estimating and Interpreting Heterogeneous Treatment Effects of Laparoscopic Surgery for Cholecystitis Patients](https://mlsanity.com/paper/2311.04359/) (2023)
- [Efficient and Robust Semi-supervised Estimation of ATE with Partially Annotated Treatment and Response](https://mlsanity.com/paper/2110.12336/) (2021)

## Built on by (most cited first, 1 of 1)

- [Estimating Long-term Heterogeneous Dose-response Curve: Generalization Bound Leveraging Optimal Transport Weights](https://mlsanity.com/paper/2406.19195/) (2024)

## Abstract (arXiv)

In many real-world causal inference applications, the primary outcomes (labels) are often partially missing, especially if they are expensive or difficult to collect. If the missingness depends on covariates (i.e., missingness is not completely at random), analyses based on fully observed samples alone may be biased. Incorporating surrogates, which are fully observed post-treatment variables related to the primary outcome, can improve estimation in this case. In this paper, we study the role of surrogates in estimating continuous treatment effects and propose a doubly robust method to efficiently incorporate surrogates in the analysis, which uses both labeled and unlabeled data and does not suffer from the above selection bias problem. Importantly, we establish the asymptotic normality of the proposed estimator and show possible improvements on the variance compared with methods that solely use labeled data. Extensive simulations show our methods enjoy appealing empirical performance.