Cerqua Augusto, Di Stefano Roberta, Mattera Raffaele
arXiv 13 Sep 2024 · Econometrics
arXiv:2409.08773 · PDF · DOI · OpenAlex · Extracted main text
Many treatments are non-randomly assigned, continuous in nature, and exhibit heterogeneous effects even at identical treatment intensities. Taken together, these characteristics pose significant challenges for identifying causal effects, as no existing estimator can provide an unbiased estimate of the average causal dose-response function. To address this gap, we introduce the Clustered Dose-Response Function (Cl-DRF), a novel estimator designed to discern the continuous causal relationships between treatment intensity and the dependent variable across different subgroups. This approach leverages both theoretical and data-driven sources of heterogeneity and operates under relaxed versions of the conditional independence and positivity assumptions, which are required to be met only within each identified subgroup. To demonstrate the capabilities of the Cl-DRF estimator, we present both simulation evidence and an empirical application examining the impact of European Cohesion funds on economic growth.
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| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Sugasawa, Shonosuke (2021) Grouped heterogeneous mixture modeling for clustered data | 1.000 | 6 | 3 | 100% |
| 2 | Wu, Xiao and Mealli, Fabrizia and Kioumourtzoglou, Marianthi-Anna an… (2024) Matching on generalized propensity scores with continuous exposures | 1.000 | 5 | 4 | 100% |
| 3 | Hirano, Keisuke and Imbens, Guido W (2004) The propensity score with continuous treatments | 0.916 | 26 | 7 | 77% |
| 4 | Huling, Jared D and Greifer, Noah and Chen, Guanhua (2024) Independence weights for causal inference with continuous treatments | 0.883 | 16 | 5 | 69% |
| 5 | Athey, Susan and Imbens, Guido (2016) Recursive partitioning for heterogeneous causal effects | 0.874 | 5 | 2 | 100% |
| 6 | Fong, Christian and Hazlett, Chad and Imai, Kosuke (2018) Covariate balancing propensity score for a continuous treatment: Application to the efficacy of political advertisements | 0.874 | 5 | 2 | 100% |
| 7 | Becker, Sascha O and Egger, Peter H and Von Ehrlich, Maximilian (2012) Too much of a good thing? On the growth effects of the EU's regional policy | 0.811 | 4 | 2 | 100% |
| 8 | Branson, Zach and Kennedy, Edward H and Balakrishnan, Sivaraman and… (2024) Causal effect estimation after propensity score trimming with continuous treatments | 0.737 | 3 | 2 | 100% |
| 9 | Kennedy, Edward H and Ma, Zongming and McHugh, Matthew D and Small,… (2017) Non-parametric methods for doubly robust estimation of continuous treatment effects | 0.737 | 3 | 2 | 100% |
| 10 | Wager, Stefan and Athey, Susan (2018) Estimation and inference of heterogeneous treatment effects using random forests | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 45 scored citations.