Notes on Causation, Comparison, and Regression
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866916107655315456 |
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| author | Chattopadhyay, Ambarish Zubizarreta, Jose R. |
| author_facet | Chattopadhyay, Ambarish Zubizarreta, Jose R. |
| contents | Comparison and contrast are the basic means to unveil causation and learn which treatments work. To build good comparison groups, randomized experimentation is key, yet often infeasible. In such non-experimental settings, we illustrate and discuss diagnostics to assess how well the common linear regression approach to causal inference approximates desirable features of randomized experiments, such as covariate balance, study representativeness, interpolated estimation, and unweighted analyses. We also discuss alternative regression modeling, weighting, and matching approaches and argue they should be given strong consideration in empirical work. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_14118 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Notes on Causation, Comparison, and Regression Chattopadhyay, Ambarish Zubizarreta, Jose R. Methodology Applications Comparison and contrast are the basic means to unveil causation and learn which treatments work. To build good comparison groups, randomized experimentation is key, yet often infeasible. In such non-experimental settings, we illustrate and discuss diagnostics to assess how well the common linear regression approach to causal inference approximates desirable features of randomized experiments, such as covariate balance, study representativeness, interpolated estimation, and unweighted analyses. We also discuss alternative regression modeling, weighting, and matching approaches and argue they should be given strong consideration in empirical work. |
| title | Notes on Causation, Comparison, and Regression |
| topic | Methodology Applications |
| url | https://arxiv.org/abs/2305.14118 |