Does Regression Produce Representative Estimates of Causal Effects

By P. M. Aronow, Cyrus Samii. 2016

166 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b3b1e78831b5d31691a515261e52e8ead6391b2e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Susan Athey
2. Guido W. Imbens
3. Kosuke Imai
4. In Song Kim
5. Stefan Wager
6. Mihaela van der Schaar
7. Ahmed M. Alaa
8. Judea Pearl

In Statistics and Probability

1. Susan Athey
2. Kosuke Imai
3. Guido W. Imbens
4. Stefan Wager
5. In Song Kim
6. Mihaela van der Schaar
7. M. D. Cattaneo
8. José R. Zubizarreta

Citations in the corpus

• Contamination Bias in Linear Regressions (2021, unknown): “This extends the Angrist (1998) result to a general control specification; versions of this extension appear in, for instance, Angrist and Krueger (1999), Angrist and Pischke (2009, Chapter 3.3), and Aronow and Samii (2016).”
• On the Use of Two-Way Fixed Effects Regression Models for Causal Inference with Panel Data (2020, methodology): “…Kim 2019) to the 2FE estimator in order to understand the causal interpretation of these widely used estimators within the nonparametric framework (see, e.g., Humphreys 2009; Aronow and Samii 2015; Solon, Haider, and Wooldridge 2015, for related work on causal inference with crosssectional data).”
• Design-based Analysis in Difference-In-Differences Settings with Staggered Adoption (2018, methodology): “Implicitly we have already made a sutva-type assumption (Rubin [1978], Imbens and Rubin [2015]) that units are not affected by the treatments (adoption dates) for other units.”
• Interpreting OLS Estimands When Treatment Effects Are Heterogeneous: Smaller Groups Get Larger Weights (2018, unknown): “Linearity of E (d | X ) is assumed in Aronow and Samii (2016) and Abadie et al. (2020).”
• When Should We Use Unit Fixed Effects Regression Models for Causal Inference with Longitudinal Data? (2019, background): “Our theoretical results also extend the weighted regression results available in the literature for causal inference with cross-section data to longitudinal studies (e.g., Aronow and Samii 2015; Humphreys 2009; Solon, Haider, and Wooldridge 2015).”