SOTAVerified

Causal Inference

Causal inference is the task of drawing a conclusion about a causal connection based on the conditions of the occurrence of an effect.

( Image credit: Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1326–1350 of 1722 papers

TitleStatusHype
Split-Treatment Analysis to Rank Heterogeneous Causal Effects for Prospective Interventions—0
Incorporating Causal Effects into Deep Learning Predictions on EHR Data—0
Causal Expectation-MaximisationCode0
Doubly Robust Off-Policy Learning on Low-Dimensional Manifolds by Deep Neural Networks—0
High-Dimensional Feature Selection for Sample Efficient Treatment Effect Estimation—0
Causal Campbell-Goodhart's law and Reinforcement LearningCode0
Exploring Logically Dependent Multi-task Learning with Causal Inference—0
Structural Causal Model with Expert Augmented Knowledge to Estimate the Effect of Oxygen Therapy on Mortality in the ICU—0
CaM-Gen:Causally-aware Metric-guided Text Generation—0
Counterfactual Representation Learning with Balancing Weights—0
Poincare: Recommending Publication Venues via Treatment Effect EstimationCode0
Causal Discovery using Compression-Complexity MeasuresCode0
Interpretable Machine Learning -- A Brief History, State-of-the-Art and Challenges—0
Causal Inference in the Presence of Interference in Sponsored Search Advertising—0
Double Robust Representation Learning for Counterfactual PredictionCode0
Causal Multi-Level Fairness—0
Comparison between instrumental variable and mediation-based methods for reconstructing causal gene networks in yeastCode0
Causal Feature Selection with Dimension Reduction for Interpretable Text Classification—0
The Adaptive Doubly Robust Estimator for Policy Evaluation in Adaptive Experiments and a Paradox Concerning Logging Policy—0
Entropic Causal Inference for Neurological Applications—0
How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference—0
Targeted VAE: Structured Inference and Targeted Learning for Causal Parameter Estimation—0
Targeted VAE: Variational and Targeted Learning for Causal InferenceCode0
Causal Intervention for Weakly-Supervised Semantic Segmentation—0
Adjusting for Confounders with Text: Challenges and an Empirical Evaluation Framework for Causal Inference—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAverage Treatment Effect Error0.96—Unverified
2Balancing Linear RegressionAverage Treatment Effect Error0.93—Unverified
3k-NNAverage Treatment Effect Error0.79—Unverified
4CEVAEAverage Treatment Effect Error0.46—Unverified
5Balancing Neural NetworkAverage Treatment Effect Error0.42—Unverified
6Causal ForestAverage Treatment Effect Error0.4—Unverified
7BCAUS DRAverage Treatment Effect Error0.29—Unverified
8TARNetAverage Treatment Effect Error0.28—Unverified
9Counterfactual Regression + WASSAverage Treatment Effect Error0.27—Unverified
10MTDL-KNNAverage Treatment Effect Error0.23—Unverified
#ModelMetricClaimedVerifiedStatus
1CFR WASSAverage Treatment Effect on the Treated Error0.09—Unverified
2CFR MMDAverage Treatment Effect on the Treated Error0.08—Unverified
3BARTAverage Treatment Effect on the Treated Error0.08—Unverified
4GANITEAverage Treatment Effect on the Treated Error0.06—Unverified
5BCAUSSAverage Treatment Effect on the Treated Error0.05—Unverified
#ModelMetricClaimedVerifiedStatus
1BARTAverage Treatment Effect Error0.34—Unverified
2OLS with separate regressors for each treatmentAverage Treatment Effect Error0.31—Unverified
3Average Treatment Effect Error-0.23—Unverified