Modeling Heterogeneous Treatment Effects in Survey Experiments with Bayesian Additive Regression Trees

By Donald P. Green, H. Kern. 2012

312 citations recorded by the source corpus.

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Citations in the corpus

Missing Data Imputation for Supervised Learning (2016, background): “Methodological applications of supervised learning include the estimation of heterogeneous treatment effects (Imai and Strauss, 2011; Green and Kern, 2012; Imai et al., 2013; Grimmer et al., 2014), text analysis (Quinn et al.”
Local Linear Forests (2018, background): “This change had a well-documented effect on responses due to the negative perception many Americans have about welfare; moreover, there is evidence of heterogeneity in the CATE surface [Green and Kern, 2012].”
Metalearners for estimating heterogeneous treatment effects using machine learning (2017, background): “[6] Green DP, Kern HL (2012) Modeling heterogeneous treatment effects in survey experiments with bayesian additive regression trees.”
The Econometrics of Randomized Experiments (2016, background): “Taking the Bayesian perspective, Green and Kern (2012) and Hill (2011) have proposed the use of forest-based algorithms for estimating heterogeneous treatment effects.”
The State of Applied Econometrics - Causality and Policy Evaluation (2016, neutral): “, Hahn [1998], Hirano et al. [2001]. The specific implementations in those papers, relying on kernel or series estimation of the propensity score, would be unlikely to work in settings with many covariates.”
Recursive partitioning for heterogeneous causal effects (2015, methodology): “Green and Kern (26) use Bayesian additive regression trees to model treatment effect heterogeneity.”
A Nonparametric Bayesian Analysis of Heterogenous Treatment Effects in Digital Experimentation (2014, background): “The framework implied by (18) is related to a semiparametric literature (e.g., Hill 2011; Green and Kern 2012; Grimmer, Messing, and Westwood 2013; Imai and Ratkovic 2013) that studies the difference between ﬂexible regression functions in each of the treatment groups.”
CAUSAL INTERPRETATIONS OF BLACK-BOX MODELS (2019, background): “Machine learning algorithms may also help us to discover heterogeneity in causal relations and optimize treatment decision (Hill, 2011, Shortreed et al., 2011, Green and Kern, 2012, Zhao et al., 2012, Wager and Athey, 2018).”
From Local to Global: External Validity in a Fertility Natural Experiment (2015, unknown): “Crump et al. (2008), Green and Kern (2012), and Imai and Ratkovic (2013) developed nonparametric methods for characterizing effect heterogeneity, including sieve estimators, Bayesian additive regression trees, and support vector machines, respectively.”
Combining observational and experimental data to find heterogeneous treatment effects (2016, background): “Keywords Heterogeneous treatment effects; Personalization”
Causal Interaction in Factorial Experiments: Application to Conjoint Analysis (2018, unknown): “Much of this literature, however, focuses on the interaction between a single treatment and pretreatment covariates (e.g., Hill 2012; Green and Kern 2012; Wager and Athey 2017; Grimmer, Messing, and West-wood 2017) or a dynamic setting where a sequence of treatment decisions is optimized (e.g.,…”