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

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Showing 401–425 of 1722 papers

TitleStatusHype
Causal Inference of Script Knowledge—0
Causal Inference on Discrete Data via Estimating Distance Correlations—0
Causal Inference on Investment Constraints and Non-stationarity in Dynamic Portfolio Optimization through Reinforcement Learning—0
Causal Inference on Multivariate and Mixed-Type Data—0
Causal Inference on Outcomes Learned from Text—0
Causal Inference on Time Series using Restricted Structural Equation Models—0
Argus Inspection: Do Multimodal Large Language Models Possess the Eye of Panoptes?—0
Causal Estimation with Functional Confounders—0
AFA-PredNet: The action modulation within predictive coding—0
Real-Time Anomaly Detection with Synthetic Anomaly Monitoring (SAM)—0
Accounting for Missing Covariates in Heterogeneous Treatment Estimation—0
Causal Inference through a Witness Protection Program—0
Causal Effect Variational Autoencoder with Uniform Treatment—0
Estimation and Inference on Nonlinear and Heterogeneous Effects—0
A Review of Generalizability and Transportability—0
Causal Inference Tools for a Better Evaluation of Machine Learning—0
Causal Inference Under Approximate Neighborhood Interference—0
Causal Inference under Data Restrictions—0
Causal Inference Under Interference And Network Uncertainty—0
Causal Inference under Interference through Designed Markets—0
Causal Inference under Networked Interference and Intervention Policy Enhancement—0
Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach—0
Causal Effect Identification from Multiple Incomplete Data Sources: A General Search-based Approach—0
Causal Effect Estimation with Variational AutoEncoder and the Front Door Criterion—0
A rational model of causal inference with continuous causes—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