Estimating individual treatment effect: generalization bounds and algorithms

By Uri Shalit, Fredrik D. Johansson, David A. Sontag. 2016

1,270 citations recorded by the source corpus.

Subfields: Causal Inference & Discovery, Statistical Learning Theory

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Citations in the corpus

Causal Effect Inference with Deep Latent-Variable Models (2017, positive): “For a continuous outcome we parametrize the probability distribution as a Gaussian with its mean given by a TARnet [48] architecture, i.”
A theory of independent mechanisms for extrapolation in generative models (2020, background): “Deep neural network have been leveraged in causal inference for learning causal graphs between observed variables (Lopez-Paz and Oquab 2016) and associated causal effects (Louizos et al. 2017; Shalit, Johansson, and Sontag 2017; Kocaoglu et al. 2017; Lachapelle et al. 2019; Zhu, Ng, and Chen 2019).”
Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders (2019, methodology): “Related Work Previous methods for causal inference mostly focused on the static setting (Hill, 2011; Wager & Athey, 2017; Alaa & van der Schaar, 2017; Shalit et al., 2017; Yoon et al., 2018; Alaa & Schaar, 2018; Zhang et al., 2020; Bica et al., 2020c), and less attention has been given to the time s”
A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms (2019, methodology): “Shalit et al. (2017) propose a technique called counterfactual regression for estimating individual treatment eﬀects from observational data.”
Deep-Treat: Learning Optimal Personalized Treatments From Observational Data Using Neural Networks (2018, background): “(Shalit, Johansson, and Sontag 2017) recently extends (Johansson, Shalit, and Sontag 2016).”
Deep Neural Networks for Estimation and Inference: Application to Causal Effects and Other Semiparametric Estimands (2018, background): “Deep neural networks have been argued (experimentally) to outperform the previous state-of-the-art in causal inference (Westreich et al., 2010; Johansson et al., 2016; Shalit et al., 2017; Hartford et al., 2017).”
Causal Interpretability for Machine Learning - Problems, Methods and Evaluation (2020, methodology): “this work leverages the algorithm proposed in [92] to learn an oracle function f which estimates the causal effect of a treatment for any observed instance and then learn an interpretable function f ′ to estimate f .”
Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations (2020, methodology): “A large number of methods have been proposed for performing 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…”
Deep Counterfactual Networks with Propensity-Dropout (2017, methodology): “…of inferring individualized treatment effects from observational datasets is a fundamental problem in many domains such as precision medicine (Shalit et al., 2017), econometrics (Abadie & Imbens, 2016), social sciences (Athey & Imbens, 2016), and computational advertising (Bottou et al.,…”
More Robust Doubly Robust Off-policy Evaluation (2018, background): “The OPE problem can also be viewed as a form of counterfactual reasoning to infer the causal effect of a new treatment from historical data (e.g., Bottou et al. 2013; Shalit et al. 2017; Louizos et al. 2017).”
Representation Balancing MDPs for Off-Policy Policy Evaluation (2018, methodology): “Our work is inspired by recent advances[19, 11, 12] in estimating conditional averaged treatment effects (CATE), also known as heterogeneous treatment effects (HTE), in the contextual bandit setting with a single (typically binary) action choice.”
Quasi-oracle estimation of heterogeneous treatment effects (2017, background): “…2017; Hill, 2011), random forests (Athey, Tibshirani, and Wager, 2018; Wager and Athey, 2017), boosting (Powers et al., 2018), neural networks (Shalit, Johansson, and Sontag, 2017), etc., as well as combinations thereof (K¨unzel et al., 2017; Luedtke and van der Laan, 2016); see Dorie et al.…”
A Survey of Learning Causality with Data (2018, methodology): “ions for features X. Representations for the text features Xt and the other features Xe are learned separately and concatenated to infer potential outcomes. Counterfactual Regression [69], the TARnet [124] and the Causal Effect Variational Autoencoder (CEVAE) [84], we are able to learn a representat”
Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks (2020, background): “This is an extension of the architecture in [20].”
A Minimax Surrogate Loss Approach to Conditional Difference Estimation (2018, background): “Recently there has been a lot of work on subgroup identification [16, 18, 25] and other types of nonlinear predictors of causal effects [3, 19, 2, 36, 34].”
Deep IV: A Flexible Approach for Counterfactual Prediction (2017, methodology): “…approaches to using machine learning methods such as trees (Wager & Athey, 2015; Athey & Imbens, 2016) and deep networks (Johansson et al., 2016; Shalit et al., 2016) for causal inference in observational data leverage an “unconfoundedness” assumption that the treatment is conditionally…”
Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design (2018, methodology): “…Causal forests (Wager & Athey, 2017), Bayesian causal forests (Hahn et al., 2017)), methods based on deep learning (CFR Wass., CFR MMD, BNN, TARnet (Shalit et al., 2017)), multivariate additive regression splines (MARS) (Powers et al., 2017), Gaussian processes (CMGP) (Alaa & van der Schaar,…”
Adapting Neural Networks for the Estimation of Treatment Effects (2019, methodology): “Our main baseline is an implementation of the 2-headed TARNET architecture from Shalit et al. [ Sha + 16 ] .”