A Nonparametric Bayesian Analysis of Heterogenous Treatment Effects in Digital Experimentation

By Matthew A. Taddy, Matt Gardner, Liyun Chen, Denise Draper. 2014

59 citations recorded by the source corpus.

Subfields: Statistics and Probability, Probabilistic & Bayesian Methods

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Citations in the corpus

Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands (2018, background): “This is particularly important in the Internet age, where experimentation is common, vast amounts of data are available, and effects are often small in magnitude (Taddy et al., 2015).”
Causal Falling Rule Lists (2015, background): “…of differential treatment effect has been of recent interest in social science, medical, and marketing domains (Brodersen et al. 2015; Sun et al. 2014; Cai et al. 2011; Foster, Taylor, and Ruberg 2011; Taddy et al. 2014), precisely so that the relevant subgroups can be given priority treatment.”
Automated versus Do-It-Yourself Methods for Causal Inference: Lessons Learned from a Data Analysis Competition (2017, background): “Nonparametric modeling of the response surface has also received some attention over the past decade (Hill, 2011; Wager and Athey, 2015; Taddy et al., 2016).”
Some methods for heterogeneous treatment effect estimation in high dimensions (2017, methodology): “Hill (2011) and Green and Kern (2012) approached the problem using Bayesian additive regression trees (Chipman et al., 1998), and Taddy et al. (2016) proposed a method based on Bayesian forests.”
Econometric Methods for Program Evaluation (2018, background): “Much of the current research in this area develops machine learning methods to estimate heterogeneous treatment effects in contexts with many covariates (see, e.g., Athey and Imbens, 2016; Wager and Athey, 2017; Taddy et al., 2016).”
Bayesian regression tree models for causal inference: regularization, confounding, and heterogeneous effects (2017, unknown): “Like us, Taddy et al. (2016) focuses on estimating heterogeneous effects, but they analyze data from experiments, whereas our data are observational.”
Assessing Treatment Effect Variation in Observational Studies: Results from a Data Challenge (2019, methodology): “We refer to Biau and Scornet (2016) for a recent overview of random forests and note that, throughout, all our forests are “honest” in the sense of Wager and Athey (2018). When working with clustered data, we adapt the random forest algorithm as follows.”
Bayesian and Empirical Bayesian Forests (2015, methodology): “Instead, we follow the approach used in Lancaster (2003), Poirier (2011), and Taddy et al. (2014): derive a ﬁrst-order Taylor approximation to the function σ j and describe the posterior for that related functional.”