Philipp Bach, Victor Chernozhukov, Carlos Cinelli, Lin Jia, Sven Klaassen, Nils Skotara, Martin Spindler
arXiv 10 Oct 2025 · Econometrics
arXiv:2510.09109 · PDF · DOI · OpenAlex · Extracted main text
Causal Machine Learning has emerged as a powerful tool for flexibly estimating causal effects from observational data in both industry and academia. However, causal inference from observational data relies on untestable assumptions about the data-generating process, such as the absence of unobserved confounders. When these assumptions are violated, causal effect estimates may become biased, undermining the validity of research findings. In these contexts, sensitivity analysis plays a crucial role, by enabling data scientists to assess the robustness of their findings to plausible violations of unconfoundedness. This paper introduces sensitivity analysis and demonstrates its practical relevance through a (simulated) data example based on a use case at Booking.com. We focus our presentation on a recently proposed method by Chernozhukov et al. (2023), which derives general non-parametric bounds on biases due to omitted variables, and is fully compatible with (though not limited to) modern inferential tools of Causal Machine Learning. By presenting this use case, we aim to raise awareness of sensitivity analysis and highlight its importance in real-world scenarios.
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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.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Victor Chernozhukov, Carlos Cinelli, Whitney Newey, Amit Sharma, and… (2023) Long story short: Omitted variable bias in causal machine learning, 2023 self | 0.980 | 17 | 6 | 94% |
| 2 | Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo,… (2018) Double/debiased machine learning for treatment and structural parameters, 2018 self | 0.874 | 6 | 2 | 100% |
| 3 | Carlos Cinelli and Chad Hazlett (2020) Making sense of sensitivity: Extending omitted variable bias self | 0.874 | 5 | 2 | 100% |
| 4 | Philipp Bach, Victor Chernozhukov, Malte S. Kurz, and Martin Spindler (2022) DoubleML – An object-oriented implementation of double machine learning in Python self | 0.737 | 3 | 2 | 100% |
| 5 | Guido W Imbens (2003) Sensitivity to exogeneity assumptions in program evaluation | 0.737 | 3 | 2 | 100% |
| 6 | Carlos Cinelli and Chad Hazlett (2022) An omitted variable bias framework for sensitivity analysis of instrumental variables self | 0.644 | 2 | 2 | 100% |
| 7 | Philipp Bach, Malte S. Kurz, Victor Chernozhukov, Martin Spindler, a… (2024) DoubleML: An object-oriented implementation of double machine learning in R self | 0.585 | 3 | 1 | 100% |
| 8 | Victor Chernozhukov, Whitney K Newey, and Rahul Singh (2022) Automatic debiased machine learning of causal and structural effects self | 0.511 | 2 | 1 | 100% |
| 9 | Joshua D Angrist and Jörn-Steffen Pischke (2009) Mostly harmless econometrics: An empiricist's companion | 0.405 | 1 | 1 | 100% |
| 10 | Tyler J. Vanderweele and Onyebuchi A. Arah (2011) Bias formulas for sensitivity analysis of unmeasured confounding for general outcomes, treatments, and confounders | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 33 scored citations.