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Beyond Linearity: Semiparametric Solutions to Contamination Bias with Multi-valued Treatments

Michel Csillag Finger, Vitor Possebom

arXiv 17 Sep 2026 · Econometrics

arXiv:2609.20473 · PDF · Extracted main text

Abstract

We examine semiparametric solutions to contamination bias for nonbinary treatments. Deepening the discussion by Goldsmith-Pinkham et al. (2024), we detail how spline functions approximate conditional expectation and propensity score functions under weak functional-form assumptions. Reanalyzing 18 regressions across 11 studies, we compare standard linear regressions against parametric and semiparametric versions of three contamination-robust estimators. We document large point-estimate discrepancies between parametric and semiparametric approaches and find that adopting flexible semiparametric solutions may not increase statistical uncertainty substantially. We recommend that researchers verify the robustness of their conclusions to the use of semiparametric tools that address contamination bias.

Citation extraction

27
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appendix boundary found by appendix_command · 66% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Paul Goldsmith-Pinkham and Peter Hull and Michal Kolesár (2024) Contamination Bias in Linear Regressions0.99241798%
2Weisburst, Emily K (2019) Police Use of Force as an Extension of Arrests: Examining Disparities across Civilian and Officer Race0.9098375%
3Michal Kolesár (2024) multe: Multiple Treatment Effects Regression0.84333100%
4Andy Brownback and Sally Sadoff Improving College Instruction through Incentives0.5114225%
5Bursztyn, Leonardo and Fiorin, Stefano and Gottlieb, Daniel and Kanz… (2019) Moral incentives in credit card debt repayment: Evidence from a field experiment0.5114225%
6Xiaohong Chen (2007) Chapter 76 Large Sample Sieve Estimation of Semi-Nonparametric Models0.51121100%
7Ackerberg, Daniel and Chen, Xiaohong and Hahn, Jinyong (2012) A Practical Asymptotic Variance Estimator for Two-Step Semiparametric Estimators0.40511100%
8Bertrand, Marianne and Duflo, Esther and Mullainathan, Sendhil (2004) How Much Should We Trust Differences-In-Differences Estimates?0.40511100%
9Tamer Cetin (2025) Debiased Machine Learning for Contamination-Free Causal Estimation with Discrete and Continuous Treatments0.40511100%
10Andrew Gelman and Guido Imbens (2019) Why High-Order Polynomials Should Not Be Used in Regression Discontinuity Designs0.40511100%

Showing the top 10 of 27 scored citations.