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 901–925 of 1722 papers

TitleStatusHype
Potential Field as Scene Affordance for Behavior Change-Based Visual Risk Object Identification—0
Potential weights and implicit causal designs in linear regression—0
Practically Effective Adjustment Variable Selection in Causal Inference—0
Practical programming research of Linear DML model based on the simplest Python code: From the standpoint of novice researchers—0
Predictive Incrementality by Experimentation (PIE) for Ad Measurement—0
PresAIse, A Prescriptive AI Solution for Enterprises—0
Prescriptive maintenance with causal machine learning—0
Principal Fairness for Human and Algorithmic Decision-Making—0
Private Causal Inference—0
Privacy-Preserving Causal Inference via Inverse Probability Weighting—0
Private measurement of nonlinear correlations between data hosted across multiple parties—0
Private Private Information—0
Proactive Recommendation in Social Networks: Steering User Interest via Neighbor Influence—0
From Probability to Counterfactuals: the Increasing Complexity of Satisfiability in Pearl's Causal Hierarchy—0
Probabilistic Easy Variational Causal Effect—0
Probabilistic Matching: Causal Inference under Measurement Errors—0
Probabilistic Temporal Prediction of Continuous Disease Trajectories and Treatment Effects Using Neural SDEs—0
Program Evaluation and Causal Inference with High-Dimensional Data—0
Program Evaluation with Remotely Sensed Outcomes—0
Projected State-action Balancing Weights for Offline Reinforcement Learning—0
Projecting infinite time series graphs to finite marginal graphs using number theory—0
Projection Pursuit Density Ratio Estimation—0
Promises and Challenges of Causality for Ethical Machine Learning—0
Prospective Artificial Intelligence Approaches for Active Cyber Defence—0
Proximal Causal Learning of Conditional Average Treatment Effects—0
Show:102550
← PrevPage 37 of 69Next →
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