Subgroup Analysis via Recursive Partitioning

By Xiaogang Su, Chih-Ling Tsai, Hansheng Wang, David M. Nickerson, Bogong Li. 2009

405 citations recorded by the source corpus.

Subfields: Statistics and Probability, Causal Inference & Discovery

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Citations in the corpus

Generalized random forests (2016, methodology): “…regression tasks, there has been a steady stream of papers proposing new splitting rules for other problems, including Athey and Imbens (2016) and Su et al. (2009) for treatment effect estimation, Beygelzimer and Langford (2009) and Kallus (2016) for personalized policy allocation, and Gordon…”
The Econometrics of Randomized Experiments (2016, methodology): “Su et al (2009) suggest using test statistics for the null hypothesis that the average treatment effect in the two subsamples are equal to zero.”
Estimating treatment effect heterogeneity in randomized program evaluation (2013, neutral): “LASSO regularization requires rescaling covariates [Tibshirani (1996)].”
Using Decision Lists to Construct Interpretable and Parsimonious Treatment Regimes (2015, methodology): “the conditional distribution of the outcome given treatment and patient covariates. Examples include estimators of the regression of an outcome on covariates, treatment, and their interactions (e.g., Su et al., 2009; Qian and Murphy, 2011; Tian et al., 2014), and estimators of point treatment eects ”
Fairer and more accurate, but for whom? (2017, methodology): “Our proposed method for characterizing dierences in fairness across models has connections to recent work on subgroup analysis and recursive binary partitioning approaches for heterogeneous treatment eect estimation [4, 21].”
Causal Falling Rule Lists (2015, methodology): “Methods have used tree structures (Su, Kang, and Levine 2012; Su and Nickerson 2009; Athey and Imbens 2015; Beygelzimer and Langford 2009) to form such treatment effect subgroups.”
Efficient Policy Learning (2017, background): “There has also been some recent interest in developing tree-based decision rules (Athey and Imbens, 2016; Kallus, 2017; Su et al., 2009).”
Quasi-oracle estimation of heterogeneous treatment effects (2017, background): “…several recent papers have proposed structural changes to various machine learning methods aimed at focusing on accurate estimation of τ ( · ) (Athey and Imbens, 2016; Hahn et al., 2017; Imai and Ratkovic, 2013; Powers et al., 2018; Shalit et al., 2017; Su et al., 2009; Wager and Athey, 2017).”
On Oblique Random Forests (2011, background): “After a large number of trees has been learned, and every pair has been tested sufficiently often, these co-occurrence frequencies can be normalized and an affinity matrix is obtained [5, 38].”
Global Model Interpretation Via Recursive Partitioning (2018, background): “Recently it is applied to study heterogeneous causal or treatment eects [1, 23].”
Learning Cost-Effective and Interpretable Treatment Regimes (2017, neutral): “This problem has been well studied [30, 8, 19, 3, 9, 34].”
Subgroup Identification and Variable Selection from Randomized Clinical Trial Data. (2013, methodology): “One simple way to do this is to use a regression tree, which splits the data into a number of regions of the covariate space (i.e. subgroups) [Negassa et al., 2005, Su et al., 2008, 2009, Foster et al., 2011, Lipkovich et al., 2011].”
Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods (2017, methodology): “For bivariate forests, a nodesize of 1 was used (following the strategy recommended by Tang and Ishwaran 2017), while for synthetic RF, the RF base learners were constructed using all possible combinations of nodesize values 1–10, 20, 30, 50, 100 and mtry values 1, 10, and 20 (for a total of 42…”
Tree-structured analysis of treatment effects with large observational data (2012, background): “The features of both the IT [13] and propensity tree (PT) can be incorporated in the MT.”
A Bayesian Nonparametic Approach for Estimating Individualized Treatment-Response Curves (2016, methodology): “Other work use tree structure to partition based on covariates that identify subpopulations with different outcomes (Foster et al., 2010; Su et al., 2009) or different conditional treatment effects (Athey and Imbens, 2015).”