Martin Huber, Kevin Kloiber, Lukas Laffers
arXiv 19 Jun 2024 · Econometrics
arXiv:2406.13826 · PDF · DOI · OpenAlex · Extracted main text
We propose a test for the identification of causal effects in mediation and dynamic treatment models that is based on two sets of observed variables, namely covariates to be controlled for and suspected instruments, building on the test by Huber and Kueck (2022) for single treatment models. We consider models with a sequential assignment of a treatment and a mediator to assess the direct treatment effect (net of the mediator), the indirect treatment effect (via the mediator), or the joint effect of both treatment and mediator. We establish testable conditions for identifying such effects in observational data. These conditions jointly imply (1) the exogeneity of the treatment and the mediator conditional on covariates and (2) the validity of distinct instruments for the treatment and the mediator, meaning that the instruments do not directly affect the outcome (other than through the treatment or mediator) and are unconfounded given the covariates. Our framework extends to post-treatment sample selection or attrition problems when replacing the mediator by a selection indicator for observing the outcome, enabling joint testing of the selectivity of treatment and attrition. We propose a machine learning-based test to control for covariates in a data-driven manner and analyze its finite sample performance in a simulation study. Additionally, we apply our method to Slovak labor market data and find that our testable implications are not rejected for a sequence of training programs typically considered in dynamic treatment evaluations.
appendix boundary found by appendix_command · 68% of the source is main text. Read the extracted text to check this.
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 | Huber and Kueck (2022) Testing the identification of causal effects in observational data | 0.941 | 12 | 5 | 83% |
| 2 | Bia, Huber, and Lafférs (2024) Double machine learning for sample selection models | 0.843 | 3 | 3 | 100% |
| 3 | Pearl (2001) Direct and indirect effects | 0.811 | 4 | 2 | 100% |
| 4 | Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey, and Robins (2018) Double/debiased machine learning for treatment and structural parameters | 0.794 | 8 | 4 | 50% |
| 5 | Imai, Keele, and Yamamoto (2010) Identification, Inference and Sensitivity Analysis for Causal Mediation Effects | 0.737 | 3 | 2 | 100% |
| 6 | Huber (2023) Causal analysis: Impact evaluation and Causal Machine Learning with applications in R self | 0.737 | 3 | 2 | 100% |
| 7 | Huber (2014) Identifying causal mechanisms (primarily) based on inverse probability weighting self | 0.644 | 2 | 2 | 100% |
| 8 | Lechner and Miquel (2010) Identification of the effects of dynamic treatments by sequential conditional independence assumptions | 0.644 | 2 | 2 | 100% |
| 9 | Robins and Greenland (1992) Identifiability and Exchangeability for Direct and Indirect Effects | 0.644 | 2 | 2 | 100% |
| 10 | Pearl (2000) Causality: Models, Reasoning, and Inference | 0.585 | 3 | 1 | 100% |
Showing the top 10 of 56 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | 2603.04109 | 0.000 | 2 | 1 |