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 651–675 of 1722 papers

TitleStatusHype
Causal Information Bottleneck Boosts Adversarial Robustness of Deep Neural Network—0
Unveiling Environmental Sensitivity of Individual Gains in Influence Maximization—0
Causal Inference with Treatment Measurement Error: A Nonparametric Instrumental Variable Approach—0
Bayesian causal inference via probabilistic program synthesis—0
Causal Inference With Selectively Deconfounded Data—0
Bayesian Causal Inference in Doubly Gaussian DAG-probit Models—0
Causal inference with Machine Learning-Based Covariate Representation—0
A Bayesian Model for Bivariate Causal Inference—0
A Mixing Time Lower Bound for a Simplified Version of BART—0
Active and Passive Causal Inference Learning—0
A Causal Framework for Precision Rehabilitation—0
Correlation vs causation in Alzheimer's disease: an interpretability-driven study—0
Causal Inference with Latent Variables: Recent Advances and Future Prospectives—0
Causal Inference with Large Language Model: A Survey—0
Bayesian Causal Forests for Multivariate Outcomes: Application to Irish Data From an International Large Scale Education Assessment—0
Causal inference with imperfect instrumental variables—0
Causal Inference with Double/Debiased Machine Learning for Evaluating the Health Effects of Multiple Mismeasured Pollutants—0
Batch-Adaptive Annotations for Causal Inference with Complex-Embedded Outcomes—0
Improving the Variance of Differentially Private Randomized Experiments through Clustering—0
Causal Inference with Deep Causal Graphs—0
Causal Inference with Corrupted Data: Measurement Error, Missing Values, Discretization, and Differential Privacy—0
Causal Inference with Conditional Instruments using Deep Generative Models—0
Balanced Linear Contextual Bandits—0
A Computational Framework for Solving Nonlinear Binary OptimizationProblems in Robust Causal Inference—0
Action needed to make carbon offsets from tropical forest conservation work for climate change mitigation—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