Covariate balancing propensity score

By Kosuke Imai, Marc Ratkovic. 2014

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Citations in the corpus

The State of Applied Econometrics - Causality and Policy Evaluation (2016, neutral): “There are a number of recent reviews of the general literature (Imbens [2004], Imbens and Rubin [2015], and for a different perspective Heckman and Vytlacil [2007a,b]), and we do not review it in its entirety in this review.”
Approximate residual balancing: debiased inference of average treatment effects in high dimensions (2016, methodology): “…connection to work by Chan et al. (2016), Deville and Särndal (1992), Graham et al. (2012, 2016), Hainmueller (2012), Hellerstein and Imbens (1999), Imai and Ratkovic (2014) and Zhao (2016), who fitted propensity models to the data under a constraint that the resulting inverse propensity weights…”
Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations (2020, methodology): “Several ways of handling the selection bias involve using propensity matching (Austin, 2011; Imai & Ratkovic, 2014; Abadie & Imbens, 2016), building representations where treated and un-treated populations had similar distributions (Johansson et al., 2016; Shalit et al., 2017; Li & Fu, 2017; Yao et…”
Identifying Statistical Bias in Dataset Replication (2020, background): “ngines such as Flickr. The authors then asked annotators on Amazon Mechanical Turk (MTurk) to select the candidate 2In causal inference terms, statistic matching is an instance of covariate balancing [IR13]. 3Note that Recht et al. [Rec+19b] actually design three datasets, in order to measure the ef”
A Survey of Learning Causality with Data: Problems and Methods (2018, methodology): “CBPS [69], a method robust to misspecification of propensity score model, is proposed to model propensity scores and balance covariate simultaneously.”
Limitations of individual causal models, causal graphs, and ignorability assumptions, as illustrated by random confounding and design unfaithfulness (2015, background): “This formulation leads to numerous modifications which further improve precision and confounding control [31, 40, 42].”
Minimal dispersion approximately balancing weights: asymptotic properties and practical considerations (2017, neutral): “We defer two of these studies to Appendix D: one on the Kang and Schafer (2007) example and another on the LaLonde (1986) data set.”
A Primer on Inverse Probability of Treatment Weighting and Marginal Structural Models (2016, neutral): “…(van der Laan, Polley, & Hubbard, 2007) strongly prefer data mining techniques to break the reliance on parametric models (for a review, see McCaffrey, Ridgeway, & Morral, 2004; Lee, Lessler, & Stuart, 2010; Sekhon, 2011; Imai & Ratkovic, 2014; or Westreich, Lessler, & Jonsson Funk, 2010).”
A unifying approach for doubly-robust $\ell_1$ regularized estimation of causal contrasts (2019, methodology): “ng" property P n[˚(Z)] ˇP n 2 4 D expit hb b;˚(Z)i ˚(Z) 3 5: 10 Estimators satisfying this property have been used in Zubizarreta (2015); Athey et al. (2018); Chan et al. (2016); Tan (2017); Imai and Ratkovic (2014); Hirshberg and Wager (2017), among others. We describe the three procedures ne”
Why Propensity Scores Should Not Be Used for Matching (2019, result): “We also appreciate the insights from our collaborators on a previous related project, Carter Coberley, James E. Pope, Aaron Wells.”
Estimation of causal effects with multiple treatments: a review and new ideas (2017, background): “These include a doubly robust approach (Tan, 2010), a covariate balancing propensity score (Imai and Ratkovic, 2014), and generalized boosted models (McCaffrey, Ridgeway and Morral, 2004; McCaffrey et al., 2013), with the latter two methodologies also extending to a multiple treatments framework.”
Globally efficient non-parametric inference of average treatment effects by empirical balancing calibration weighting (2015, methodology): “…have proposed estimators by combining equations (1) and (4) in various creative manners under the propensity score framework; see Qin and Zhang (2007), Tan (2010), Chan (2012), Graham et al. (2012), Vansteelandt et al. (2012), Han and Wang (2013), Imai and Ratkovic (2014) and Chan and Yam (2014).”
Matching on Balanced Nonlinear Representations for Treatment Effects Estimation (2017, methodology): “Most recently, a covariate balancing propensity score (CBPS) method is developed to balance the distributions of two groups by weighting the covariates, and has shown promising performance [18].”
Covariate balancing propensity score for a continuous treatment: Application to the efficacy of political advertisements (2018, methodology): “In this paper, we extend the covariate balancing propensity score ( CBPS ) methodology of Imai and Ratkovic (2014) to a continuous treatment [see Fan et al. (2016) for the theoretical properties of CBPS , andImai and Ratkovic (2015) and Zhao (2016) for other extensions].”
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].”