Efficient semiparametric estimation of multi-valued treatment effects under ignorability

By M. D. Cattaneo. 2010

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Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands (2018, background): “Different treatment effect estimands are reviewed by Lechner (2001), Cattaneo (2010), and others.”
Stratification Trees for Adaptive Randomization in Randomized Controlled Trials (2018, methodology): “(2011) and Chambaz et al. (2014). Hahn et al. (2011) develop a procedure which uses the information from a first wave of the experiment in order to compute the propensity-score that minimizes the asymptotic variance of an ATE estimator, over a discrete set of covariates (i.”
Semiparametric Estimation and Inference Using Doubly Robust Moment Conditions (2013, unknown): “That same dataset was also used in a recent paper by Cattaneo (2010), who exploits the fact that mothers report their daily smoking intensity to apply his semiparametric method of estimation of multi-valued treatment effects.”
Robust Inference on Average Treatment Effects with Possibly More Covariates than Observations (2013, unknown): “A useful and natural extension would be to consider quantile treatment effects (Firpo 2007) or more generic moment condition based estimands (Cattaneo 2010).”
The Value of Knowing the Propensity Score for Estimating Average Treatment Effects (2016, background): “We remark that all the aforementioned estimators are affected by the “curse of dimensionality”, in the sense that ever-stricter regularity conditions need to be imposed (2)See Cattaneo (2010) for an earlier use of this construction in a settings with a multi-valued treatment.”
Program evaluation and causal inference with high-dimensional data (2013, methodology): “…) := ψ α V,z,gV ,mZ (W,αV (z)) and (3.12) ψαV,z,g,m(W,α) := 1(Z = z)(V − g(z,X)) m(z,X) + g(z,X)− α. (3.13) This efficient influence function was derived by Hahn (1998); it has recently been used by Cattaneo (2010) in the series context (with p n) and Rothe and Firpo (2013) in the kernel context.”
Shining a spotlight on small rural businesses: How does their performance compare with urban? (2019, methodology): “…consider differences in performance across the three LAD categories (Urban with Significant Rural, Largely Rural and Mainly Rural), due to limitations of PSM in estimating multiple treatments, we apply the Inverse Probability Weighting (IPW) approach to estimate treatment effects (Cattaneo, 2010).”
Econometric Methods for Program Evaluation (2018, methodology): “Many of these results have been extended to multi-valued treatments (Imbens, 2000; Lechner, 2001; Hirano and Imbens, 2004; Imai and van Dyk, 2004; Cattaneo, 2010; Farrell, 2015; Yang et al., 2016), to quantiles and related treatment effects (Firpo, 2007; Cattaneo, 2010), and to the analysis and inte”
Doubly robust uniform confidence band for the conditional average treatment effect function (2016, methodology): “The dataset is an excerpt from Cattaneo (2010) and is obtained from the STATA website (“http://www.stata-press.com/data/r13/ cattaneo2.dta”). Note that the dataset was originally used in Almond, Chay, and Lee (2005). We restrict our sample to white and non-Hispanic mothers, and the sample size is 37”
Estimating the Effects of Length of Exposure to Instruction in a Training Program: The Case of Job Corps (2012, methodology): “…case under an unconfoundedness assumption analogous to equation (1) are analyzed in Hirano, Imbens, and Ridder (2003) and have also been used in other settings such as multivalued treatments (Cattaneo, 2010), difference-in-difference (Abadie, 2005), and instrumental variables models (Abadie, 2003).”
Contamination Bias in Linear Regressions (2021, unknown): “…conceptually principled solution is to adapt approaches to estimating the average treatment effect (ATE) of a conditionally ignorable binary treatment (see Imbens & Wooldridge, 2009, for a review) to the multiple treatment case (e.g. Cattaneo, 2010; Chernozhukov et al., 2021; Graham & Pinto, 2022).”
Efficient Estimation of the Dose–Response Function Under Ignorability Using Subclassification on the Covariates (2011, methodology): “Using some of the asymptotic results presented in Cattaneo and Farrell (2011) for partitioning estimators, this chapter provides sufficient conditions for the efficient semiparametric estimation of the DRF.”
Doubly robust difference-in-differences estimators (2018, unknown): “…doubly robust estimators, see e.g. Robins et al. (1994), Scharfstein et al. (1999), Bang and Robins (2005), Wooldridge (2007), Chen et al. (2008), Cattaneo (2010), Graham et al. (2012, 2016), Lee et al. (2017), Słoczyński and Wooldridge (2018), Rothe and Firpo (2018), among many others; for a…”
Propensity score matching and subclassification in observational studies with multi‐level treatments (2015, unknown): “…with more than two treatment levels (exceptions include Imbens, 2000; Robins, Hernan, and Brumback, 2000; Lechner, 2001; Foster, 2003; Hirano and Imbens, 2004; Imai and Van Dyk, 2004; Cole and Frangakis, 2009; Cadarette et al., 2010; Cattaneo, 2010; Mc-Caﬀrey et al., 2013; Rassen et al., 2013).”
A GENERAL DOUBLE ROBUSTNESS RESULT FOR ESTIMATING AVERAGE TREATMENT EFFECTS (2014, methodology): “This approach to identifying quantile treatment effects was used by Cattaneo (2010), Frandsen et al. (2012), Chernozhukov et al. (2013), Frölich and Melly (2013), Donald and Hsu (2014), and Sant’Anna (2016), among others. However, only Cattaneo (2010) discussed doubly robust estimation of QTEs, whic”