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Semiparametric Estimation of Long-Term Treatment Effects

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  • Jiafeng Chen
  • David M. Ritzwoller

Abstract

Long-term outcomes of experimental evaluations are necessarily observed after long delays. We develop semiparametric methods for combining the short-term outcomes of experiments with observational measurements of short-term and long-term outcomes, in order to estimate long-term treatment effects. We characterize semiparametric efficiency bounds for various instances of this problem. These calculations facilitate the construction of several estimators. We analyze the finite-sample performance of these estimators with a simulation calibrated to data from an evaluation of the long-term effects of a poverty alleviation program.

Suggested Citation

  • Jiafeng Chen & David M. Ritzwoller, 2021. "Semiparametric Estimation of Long-Term Treatment Effects," Papers 2107.14405, arXiv.org, revised Aug 2023.
    • Handle: RePEc:arx:papers:2107.14405
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    Cited by:

    1. Yu-Shiou Willy Lin & Dae Woong Ham & Iavor Bojinov, 2026. "Benefits and Costs of Adaptive Sampling," Papers 2604.24652, arXiv.org.
    2. Xiaohong Chen & Haitian Xie, 2025. "Local Overidentification and Efficiency Gains in Modern Causal Inference and Data Combination," Cowles Foundation Discussion Papers 2467, Cowles Foundation for Research in Economics, Yale University.
    3. Yanqin Fan & Carlos A. Manzanares & Hyeonseok Park & Yuan Qi, 2026. "A Sensitivity Analysis of the Surrogate Index Approach for Estimating Long-Term Treatment Effects," Papers 2603.00580, arXiv.org.
    4. Ting-Chih Hung & Yu-Chang Chen, 2026. "The Proximal Surrogate Index: Long-Term Treatment Effects under Unobserved Confounding," Papers 2601.17712, arXiv.org.
    5. Park, Yechan & Sasaki, Yuya, 2026. "The informativeness of combined experimental and observational data under dynamic selection," Journal of Econometrics, Elsevier, vol. 254(PB).
    6. Xiaohong Chen & Haitian Xie, 2026. "On Local Overidentification and Efficiency Gains in Modern Causal Inference and Data Combination," Cowles Foundation Discussion Papers 2497, Cowles Foundation for Research in Economics, Yale University.
    7. Xiaohong Chen & Haitian Xie, 2025. "On Local Overidentification and Efficiency Gains in Modern Causal Inference and Data Combination," Papers 2510.16683, arXiv.org, revised Feb 2026.
    8. David M. Ritzwoller & Vasilis Syrgkanis, 2024. "Order-Explicit Linearization of High-Dimensional $U$-Statistics," Papers 2405.07860, arXiv.org, revised Jul 2026.
    9. Kentaro Kawato, 2025. "Balancing Weights for Causal Mediation Analysis," Papers 2512.09337, arXiv.org.
    10. David M. Ritzwoller & Joseph P. Romano, 2023. "Reproducible Aggregation of Sample-Split Statistics," Papers 2311.14204, arXiv.org, revised Nov 2024.

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    More about this item

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • O10 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - General

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