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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 976–1000 of 1722 papers

TitleStatusHype
Unfaithful Probability Distributions in Binary Triple of Causality Directed Acyclic Graph—0
Simultaneous Inference for Local Structural Parameters with Random Forests—0
Unifying Causal Inference and Reinforcement Learning using Higher-Order Category Theory—0
Universal Decision Models—0
Unveiling Causalities in SAR ATR: A Causal Interventional Approach for Limited Data—0
Unveiling the Impact of Macroeconomic Policies: A Double Machine Learning Approach to Analyzing Interest Rate Effects on Financial Markets—0
Uplift Modeling: from Causal Inference to Personalization—0
User-Oriented Smart General AI System under Causal Inference—0
Using causal inference to avoid fallouts in data-driven parametric analysis: a case study in the architecture, engineering, and construction industry—0
Using Deep Autoregressive Models as Causal Inference Engines—0
How and Why to Use Experimental Data to Evaluate Methods for Observational Causal Inference—0
Using Machine Learning to Assess Short Term Causal Dependence and Infer Network Links—0
Using Network-based Causal Inference to Detect the Sources of Contagion in the Currency Market—0
Valid Causal Inference with (Some) Invalid Instruments—0
Valid Inference After Causal Discovery—0
Variable Selection for Causal Inference via Outcome-Adaptive Random Forest—0
Variable Selection in Maximum Mean Discrepancy for Interpretable Distribution Comparison—0
Variance Reduction in Bipartite Experiments through Correlation Clustering—0
Variational Auto-Encoder Architectures that Excel at Causal Inference—0
Variational Causal Autoencoder for Interventional and Counterfactual Queries—0
Variational Deterministic Uncertainty Quantification—0
Virtual Control Group: Measuring Hidden Performance Metrics—0
Vision Paper: Causal Inference for Interpretable and Robust Machine Learning in Mobility Analysis—0
VLUCI: Variational Learning of Unobserved Confounders for Counterfactual Inference—0
Choosing with unknown causal information: Action-outcome probabilities for decision making can be grounded in causal models—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