Hi-CI: Deep Causal Inference in High Dimensions
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[1] Uri Shalit,et al. Estimating individual treatment effect: generalization bounds and algorithms , 2016, ICML.
[2] J. Robins,et al. Estimation of Regression Coefficients When Some Regressors are not Always Observed , 1994 .
[3] Peter Bühlmann,et al. Causal statistical inference in high dimensions , 2013, Math. Methods Oper. Res..
[4] Lovekesh Vig,et al. MetaCI: Meta-Learning for Causal Inference in a Heterogeneous Population , 2019, ArXiv.
[5] Lovekesh Vig,et al. MultiMBNN: Matched and Balanced Causal Inference with Neural Networks , 2020, ESANN.
[6] Isaac Dialsingh,et al. Applied Bayesian Modeling and Causal Inference from Incomplete Data Perspectives , 2005 .
[7] Joaquin Quiñonero Candela,et al. Counterfactual reasoning and learning systems: the example of computational advertising , 2013, J. Mach. Learn. Res..
[8] Chad Hazlett,et al. Covariate balancing propensity score for a continuous treatment: Application to the efficacy of political advertisements , 2018 .
[9] Zichen Zhang,et al. Reducing Selection Bias in Counterfactual Reasoning for Individual Treatment Effects Estimation , 2019, ArXiv.
[10] Foster Provost,et al. Causally motivated attribution for online advertising , 2012, ADKDD '12.
[11] Victor Chernozhukov,et al. Inference on Treatment Effects after Selection Amongst High-Dimensional Controls , 2011 .
[12] L. Xue,et al. CBPS-Based Inference in Nonlinear Regression Models with Missing Data , 2016 .
[13] Mihaela van der Schaar,et al. Deep-Treat: Learning Optimal Personalized Treatments From Observational Data Using Neural Networks , 2018, AAAI.
[14] Eustache Diemert,et al. Attribution Modeling Increases Efficiency of Bidding in Display Advertising , 2017, ADKDD@KDD.
[15] Jianqing Fan,et al. Improving Covariate Balancing Propensity Score : A Doubly Robust and Efficient Approach ∗ , 2016 .
[16] Uri Shalit,et al. Learning Representations for Counterfactual Inference , 2016, ICML.
[17] Gary King,et al. Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference , 2007, Political Analysis.
[18] Vikas Ramachandra,et al. Deep Learning for Causal Inference , 2018, ArXiv.
[19] G. Imbens,et al. The Propensity Score with Continuous Treatments , 2005 .
[20] K. Imai,et al. Covariate balancing propensity score , 2014 .
[21] Walter Karlen,et al. Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks , 2018, ArXiv.
[22] Stefan Bauer,et al. Learning Counterfactual Representations for Estimating Individual Dose-Response Curves , 2019, AAAI.
[23] A. Belloni,et al. Inference on Treatment Effects after Selection Amongst High-Dimensional Controls , 2011, 1201.0224.
[24] Douglas Galagate,et al. Causal inference with a continuous treatment and outcome: Alternative estimators for parametric dose-response functions with applications , 2016 .
[25] G. Imbens,et al. Approximate residual balancing: debiased inference of average treatment effects in high dimensions , 2016, 1604.07125.
[26] Jian Yang,et al. Causal Inference via Sparse Additive Models with Application to Online Advertising , 2015, AAAI.
[27] Joshua M. Stuart,et al. The Cancer Genome Atlas Pan-Cancer analysis project , 2013, Nature Genetics.
[28] Edward H Kennedy,et al. Non‐parametric methods for doubly robust estimation of continuous treatment effects , 2015, Journal of the Royal Statistical Society. Series B, Statistical methodology.