Inverse Probability Tilting for Moment Condition Models with Missing Data

By Bryan S. Graham, Cristine Pinto, Daniel Egel. 2012

206 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research, Statistics and Probability

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Citations in the corpus

Approximate residual balancing: debiased inference of average treatment effects in high dimensions (2016, methodology): “Similar estimators have been considered by Graham et al. (2012, 2016) and Hainmueller (2012) in a setting where exact balancing is possible, with slightly different objection functions.”
Efficient Policy Learning (2017, methodology): “, 2016b; Imai and Ratkovic, 2013), and to use methods for ê(·) that privilege balance of inversepropensity weighted features over classification accuracy (Athey et al., 2016a; Graham et al., 2012).”
Semiparametric Estimation and Inference Using Doubly Robust Moment Conditions (2013, unknown): “See for example Robins, Rotnitzky, and Zhao (1994), Robins and Rotnitzky (1995), Scharfstein, Rotnitzky, and Robins (1999), Robins and Rotnitzky (2001), Van der Laan and Robins (2003) or Tan (2006), and Wooldridge (2007) or Graham, Pinto, and Egel (2012) for applications in econometrics.”
Simultaneous inference for Best Linear Predictor of the Conditional Average Treatment Effect and other structural functions (2017, background): “In the class of general missing data models, eﬃciency bounds have been established under the assumption that the propensity score can be modeled by a ﬁnite-dimensional parameter(Graham (2011), Graham et al. (2012)), which is a restrictive condition.”
Propensity Score Weighting for Causal Inference with Clustered Data (2017, methodology): “In causal inference, calibration has been used such as in Constrained Empirical Likelihood [50], Entropy Balancing [51], Inverse Probability Tilting [52], and Covariate Balance Propensity Score of Imai and Ratkovic [53].”
Estimating Average Treatment Effects: Supplementary Analyses and Remaining Challenges (2017, background): “posed that focus directly on balancing the pretreatment variables, bypassing estimation of the propensity score (Hainmueller [2012], Zubizarreta [2015], Graham et al.”
Model-assisted inference for treatment effects using regularized calibrated estimation with high-dimensional data (2018, background): “calibrated estimation for propensity score models have been studied, sometimes independently (re)derived, in causal inference, missing-data problems, and survey sampling (e.g., Folsom 1991; Tan 2010; Graham et al. 2012; Hainmueller 2012; Imai & Ratovic 2014; Kim & Haziza 2014; Vermeulen &amp”
New Evidence on the Finite Sample Properties of Propensity Score Reweighting and Matching Estimators (2014, methodology): “…being married, an indicator for 1974 unemployment, an indicator for 1975 unemployment, 1974 earnings in thousands of dollars and its square, 1975 earnings in thousands of dollars and its square, and interactions between the 1974 and 1975 unemployment indicators and between 1974 and 1975 earnings.”
Doubly robust difference-in-differences estimators (2018, unknown): “Chapter 5 in van der Vaart (1998), Wooldridge (2007), Graham et al. (2012) and Sant’Anna et al. (2018). Assumption 4(v) is a strong overlap condition that guarantees that every unit has a positive probability of being in the comparison group.”
A GENERAL DOUBLE ROBUSTNESS RESULT FOR ESTIMATING AVERAGE TREATMENT EFFECTS (2014, background): “Hirano and Imbens (2001) provided an early application to data on right heart catheterization; however, these authors used linear models for a binary outcome, which necessarily does not fully exploit the double robustness result.”
Radius matching on the propensity score with bias adjustment: tuning parameters and finite sample behaviour (2014, background): “…Rubin 1983), kernel matching (Heckman et al. 1998a), radius matching (Rosenbaum and Rubin 1985; Dehejia and Wahba 1999), inverse probability weighting (Horvitz and Thompson 1952; Hirano et al. 2003), inverse probability tilting (Graham et al. 2012) and doubly robust estimation (Robins et al. 1992).”
Balancing Covariates via Propensity Score Weighting (2014, methodology): “Theorem 3 is distinct from the exact balance result in Graham, Pinto, and Egel (2012), which is on inverse probability weights and does not provide the explicit form of the propensity score model that would guarantee exact balance.”