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1605.03661
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Learning Representations for Counterfactual Inference
12 May 2016
Fredrik D. Johansson
Uri Shalit
David Sontag
CML
OOD
BDL
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Papers citing
"Learning Representations for Counterfactual Inference"
50 / 432 papers shown
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Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations
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200
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Adversarial Counterfactual Environment Model Learning
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218
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182
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Estimating counterfactual treatment outcomes over time in complex multiagent scenarios
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Koh Takeuchi
Atsushi Kuribayashi
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455
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04 Jun 2022
Learning Disentangled Representations for Counterfactual Regression via Mutual Information Minimization
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Bin Ma
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156
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Feature Selection for Discovering Distributional Treatment Effect Modifiers
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391
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Comparison of meta-learners for estimating multi-valued treatment heterogeneous effects
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Ramiro Lugo
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247
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184
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168
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Robust and Agnostic Learning of Conditional Distributional Treatment Effects
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Miruna Oprescu
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383
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Neuroevolutionary Feature Representations for Causal Inference
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Michael C. Burkhart
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95
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21 May 2022
Deconfounding Actor-Critic Network with Policy Adaptation for Dynamic Treatment Regimes
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217
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Causal Transformer for Estimating Counterfactual Outcomes
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273
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Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxies
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Kaiwen Wang
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355
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305
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159
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281
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Tobias Hatt
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266
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Neural Score Matching for High-Dimensional Causal Inference
International Conference on Artificial Intelligence and Statistics (AISTATS), 2022
Oscar Clivio
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Chris Holmes
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216
9
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170
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Stefan Feuerriegel
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284
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Ensemble Method for Estimating Individualized Treatment Effects
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125
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128
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Diffusion Causal Models for Counterfactual Estimation
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299
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Hierarchical Interpretation of Neural Text Classification
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Hanqi Yan
Lin Gui
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324
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Benign-Overfitting in Conditional Average Treatment Effect Prediction with Linear Regression
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187
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To Impute or not to Impute? Missing Data in Treatment Effect Estimation
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309
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376
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Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance
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205
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A Causal Lens for Controllable Text Generation
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Erran L. Li
224
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Individual Treatment Effect Estimation Through Controlled Neural Network Training in Two Stages
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122
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DRTCI: Learning Disentangled Representations for Temporal Causal Inference
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80
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Efficiently Disentangle Causal Representations
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Joel Hestness
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79
1
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BITES: Balanced Individual Treatment Effect for Survival data
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S. Solbrig
R. Lohmayer
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Rainer Spang
H. Zacharias
Michael Altenbuchinger
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128
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Deep Treatment-Adaptive Network for Causal Inference
The VLDB journal (VLDBJ), 2021
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Zhichao Wang
Shaowu Liu
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Guandong Xu
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123
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CausalMTA: Eliminating the User Confounding Bias for Causal Multi-touch Attribution
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Huzefa Rangwala
Yue Ning
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139
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Enhancing Counterfactual Classification via Self-Training
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Disentangled Counterfactual Recurrent Networks for Treatment Effect Inference over Time
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206
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213
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387
10
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194
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105
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