Matching on Balanced Nonlinear Representations for Treatment Effects Estimation

By Sheng Li, Yun Raymond Fu. 2017

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Citations in the corpus

Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations (2020, methodology): “…causal inference using observational data in the static setting (Johansson et al., 2016; Shalit et al., 2017; Alaa & van der Schaar, 2017; Li & Fu, 2017; Yoon et al., 2018; Alaa & van der Schaar, 2018; Yao et al., 2018) and only a few methods address the longitudinal setting (Xu et al.,…”
Stable Prediction across Unknown Environments (2018, methodology): “d to achieve variable balancing by reweighting observations by the inverse of propensity score. Kuang et al. [13] proposed a datadriven variable decomposition method for variable balancing. Li et al. [22] bal- anced the variables by matching on their nonlinear representation. Hainmueller [15] introd”
A Survey on Causal Inference (2020, background): “ng the relationship between the transformed space and the observed outcome. These two lines of methods have different advantages, and some recent work tries to integrate these advantages together. In [77], the balanced and nonlinear representation (BNR) is proposed to project the covariates into a b”
Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design (2018, methodology): “…increasing availability of observational data in all these domains has encouraged the development of various machine learning algorithms tailored for inferring treatment effects using observational data (e.g. (Li & Fu, 2017; Wager & Athey, 2017; Shalit et al., 2017; Alaa & van der Schaar, 2017)).”
Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks (2020, background): “, 2010; Athey and Imbens, 2016; Wager and Athey, 2018; Kallus, 2017) or building balancing representations between patients receiving the different treatments (Johansson et al., 2016; Shalit et al., 2017; Li and Fu, 2017; Yao et al., 2018).”
Representation Learning for Treatment Effect Estimation from Observational Data (2018, background): “Recent works have applied distribution balancing constraints to either the covariate space [16] or latent space [18, 23].”
Bayesian Nonparametric Causal Inference: Information Rates and Learning Algorithms (2017, background): “More recently, the machine learning community has also developed estimators for the average treatment effects that borrows ideas from representation learning, i.e. see for instance the work in [9].”