Statistical Models for Causation
By David Freedman. 2006
96 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Statistics and Probability
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
- Susan Athey
- Guido W. Imbens
- Esther Duflo
- Jasjeet S. Sekhon
- Stefan Wager
- T. Dunning
- Donald P. Green
- Abhijit Banerjee
In Statistics and Probability
- Susan Athey
- Guido W. Imbens
- Stefan Wager
- Nathan Kallus
- M. D. Cattaneo
- Jasjeet S. Sekhon
- Peng Ding
- Victor Chernozhukov
Citations in the corpus
- The Econometrics of Randomized Experiments (2016, background): “Freedman (2006) writes succintly “Experiments offer more reliable evidence on causation than observational studies.””
- Model Specification in Instrumental-Variables Regression (2008, background): “2See Heckman and Robb (1986); Imbens and Angrist (1994); Angrist, Imbens, and Rubin (1996); Rosenzweig and Wolpin (2000); Freedman (2006); Heckman, Urzua, and Vytlacil (2006).”
- Intention to treat, per protocol, as treated and instrumental variable estimators given non‐compliance and effect heterogeneity (2009, methodology): “Although authors [13, 14] have drawn attention to the limitations of PP and AT analyses, the PP approach is sometimes cautiously recommended along with ITTe, especially for equivalence trials [15, 16].”
- WORKING PAPER SERIES BETTER LATE THAN NOTHING : SOME COMMENTS ON DEATON ( 2009 ) AND HECKMAN AND URZUA (background): “…title page, acknowledgement) is unambiguous in his opening sentence, “Experiments offer more reliable evidence on causation than observational studies,” (Freedman, 2006, abstract) That is not to say that one may not choose to do an observational study for other reasons, e.g., financial costs, or…”
- A Cautionary Note on the Use of Matching to Estimate Causal Effects: An Empirical Example Comparing Matching Estimates to an Experimental Benchmark (2010, background): “For example, Green and Ensminger (2006) use matching to study the long-term behavioral effects of marijuana use among African-Americans; Glick et al.”
- Opiates for the Matches: Matching Methods for Causal Inference (2009, methodology): “Consequently, instead of the " Neyman-Rubin model, " the model is often simply called the Rubin causal model (e.g., Holland 1986) or sometimes the Neyman- Rubin-Holland model (e.g., Brady 2008) or the Neyman-Holland-Rubin model (e.g., Freedman 2006).”
- Better Late than Nothing: Some Comments on Deaton (2009) and Heckman and Urzua (2009) (2010, background): “…title page, acknowledgement) is unambiguous in his opening sentence, “Experiments offer more reliable evidence on causation than observational studies,” (Freedman, 2006, abstract) That is not to say that one may not choose to do an observational study for other reasons, e.g., financial costs, or…”
- Information Strategies and Energy Conservation Behavior: A Meta-Analysis of Experimental Studies from 1975 to 2012 (2013, unknown): “More generally, we observe strong evidence for heterogeneous responses to behavioral treatments, consistent with prior literature (see Costa and Kahn, 2010; Freedman, 2006).”
- Understanding and misunderstanding randomized controlled trials (2016, unknown): “Garfinkel and Manski (1992), Heckman (1992), and Moffitt (1992), and is recognized in the recent review by Banerjee and Duflo (2009).”