Applied Bayesian Modeling and Causal Inference From Incomplete-Data Perspectives

By Keying Ye. Technometrics, 2005

457 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/8f2f2a2620913ab3a3c189a9f02fb2035741a488

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In Artificial Intelligence

Citations in the corpus

The Power and Limits of Predictive Approaches to Observational-Data-Driven Optimization (2016, background): “If treatments are continuous, the generalized propensity score of a unit is defined as the conditional density of the unit receiving whatever treatment it did receive given the subject’s covariates (Robins et al. 2000, Hirano and Imbens 2004, Imai and Van Dyk 2004).”
An Empirical Investigation of Professional Development Effects on Literacy Instruction Using Daily Logs (2007, methodology): “Additionally, although I have demonstrated the effect of intense PD as a dichotomous treatment, the methods could be applied for a continuous variable using a generalized propensity score (see, e.g., Hirano & Imbens, 2004; Imai & VanDyk, 2004; Imbens, 1999).”
Chief Executive Officer Equity Incentives and Accounting Irregularities (2010, background): “See Hirano and Imbens (2004) for a theoretical discussion and Armstrong, Ittner, and Larcker (2009) and Armstrong, Blouin, and Larcker (2009) for examples of implementing this approach.”
On the Conditional Distribution of the Multivariate t Distribution (2016, methodology): “…property, theMVTdistribution iswidely applied in robust data analysis including linear and nonlinear regressions (Lange, Little, and Taylor 1989; Liu 1994, 2004; Gelman et al. 2014), linear mixed effects models (Pinheiro, Liu, and Wu 2001), and sample selection models (Marchenko and Genton…”
Why Propensity Scores Should Not Be Used for Matching (2019, background): “PSM is sometimes described as solving the curse of dimensionality problem, since researchers only need to condition on a scalar rather than k covariates (Dehejia, 2004).”
MICE: Multivariate Imputation by Chained Equations in R (2011, background): “Our hope is that the framework will aid the user to map out the steps needed in practical applications.”
Double Debiased Machine Learning Nonparametric Inference with Continuous Treatments (2019, unknown): “In low-dimensional settings, see Imbens (2000), Hirano and Imbens (2004), Flores (2007), and Lee (2018) for examples of a class of regression-type estimators n (cid:5) (cid:5) Galvao and Wang (2015) and Hsu et al. (2020) study a class of inverse probability weighting estimators.”
The Effect of Wealth on Individual and Household Labor Supply: Evidence from Swedish Lotteries (2017, background): “…the rela- 2The estimated effects in Imbens et al. (2001) are highly non-linear and also somewhat sensitive to the small number of individuals in the sample who won prizes exceeding $2 million USD, as well as specifications that account for non-random survey non-response (Hirano and Imbens, 2004).”
The design versus the analysis of observational studies for causal effects: parallels with the design of randomized trials (2007, unknown): “Meng [1]).”
Modeling heterogeneous treatment effects in large-scale experiments using Bayesian Additive Regression Trees (2010, background): “…other common estimands such as the average treatment effect among the treated, ATT) as a summary of the distribution of treatment effects depends on the extent and form of treatment effect heterogeneity in any given experiment (Cox 1958; see also Gelman 2004 and Horiuchi, Imai, and Taniguchi 2007).”
Prison Visitation and Recidivism (2012, methodology): “…the traditional matching approach in the binary case, the benefit of the generalized propensity score methodology lies in its greater ability, as compared to a traditional regression approach, to eliminate bias due to differences across treatment groups in the covariates (Hirano and Imbens 2004).”
The Blessings of Multiple Causes (2018, methodology): “3 Here we describe the weak version of the unconfoundedness assumption, which requires individual potential outcomes Y i ( a ) be marginally independent of the causes A i , that is, Imbens (2000) and Hirano and Imbens (2004) call this assumption weak unconfound-edness .”
Using the Propensity Score Method to Estimate Causal Effects (2013, methodology): “…certain covariates from the treated group and the control group to obtain a sample with similar distributions of covariates between the two groups.4 Rosenbaum (2004) concluded that propensity score matching can increase the robustness of the model-based adjustment and avoid unnecessarily detailed…”
Can Nonrandomized Experiments Yield Accurate Answers? A Randomized Experiment Comparing Random and Nonrandom Assignments (2008, methodology): “This was not done by Dehejia and Wahba (1999), Heckman et al. (1997), or Hill et al. (2004), or in any other such studies.”