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 1426–1450 of 1722 papers

TitleStatusHype
Distributional synthetic controls—0
Inferring Individual Level Causal Models from Graph-based Relational Time Series—0
Combining Offline Causal Inference and Online Bandit Learning for Data Driven Decision—0
The Counterfactual χ-GAN—0
Quantile Causal DiscoveryCode0
Localized Debiased Machine Learning: Efficient Inference on Quantile Treatment Effects and BeyondCode0
Counterfactual Evaluation of Treatment Assignment Functions with Networked Observational Data—0
Causal Inference and Data Fusion in Econometrics—0
Network Data—0
More Efficient Off-Policy Evaluation through Regularized Targeted Learning—0
MetaCI: Meta-Learning for Causal Inference in a Heterogeneous Population—0
Using Machine Learning to Assess Short Term Causal Dependence and Infer Network Links—0
Variance Reduction in Bipartite Experiments through Correlation Clustering—0
Deep representation learning for individualized treatment effect estimation using electronic health recordsCode0
Perceiving the arrow of time in autoregressive motion—0
A Normative Theory for Causal Inference and Bayes Factor Computation in Neural CircuitsCode0
High Dimensional M-Estimation with Missing Outcomes: A Semi-Parametric Framework—0
A Causal Inference Method for Reducing Gender Bias in Word Embedding RelationsCode0
Causality for Machine LearningCode0
Causally Denoise Word Embeddings Using Half-Sibling RegressionCode0
Algorithmic Bias in Recidivism Prediction: A Causal Perspective—0
RSM-GAN: A Convolutional Recurrent GAN for Anomaly Detection in Contaminated Seasonal Multivariate Time Series—0
Causal Inference Under Approximate Neighborhood Interference—0
Bayesian nonparametric discontinuity designCode0
Selective machine learning of doubly robust functionals—0
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All datasetsIHDPJobsIDHP

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