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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

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Showing 1376–1400 of 1722 papers

TitleStatusHype
Intelligent Credit Limit Management in Consumer Loans Based on Causal Inference—0
Meta Learning for Causal Direction—0
Assessing External Validity Over Worst-case SubpopulationsCode0
Learning to search efficiently for causally near-optimal treatmentsCode0
A Perspective on Gaussian Processes for Earth Observation—0
Estimation of Causal Effects in the Presence of Unobserved Confounding in the Alzheimer's ContinuumCode0
A Causally Formulated Hazard Ratio Estimation through Backdoor Adjustment on Structural Causal Model—0
Mitigating Bias in Online Microfinance Platforms: A Case Study on Kiva.org—0
Valid Causal Inference with (Some) Invalid Instruments—0
Causal inference of brain connectivity from fMRI with ψ-Learning Incorporated Linear non-Gaussian Acyclic Model (ψ-LiNGAM)Code0
Causal Inference with Deep Causal Graphs—0
Targeting Learning: Robust Statistics for Reproducible Research—0
Uncertainty promotes neuroreductionism: A behavioral online study on folk psychological causal inference from neuroimaging data—0
Regret Minimization for Causal Inference on Large Treatment Space—0
Wasserstein Random Forests and Applications in Heterogeneous Treatment EffectsCode0
Ensemble Learning with Statistical and Structural Models—0
Identifying Causal Structure in Dynamical SystemsCode0
Causality and Batch Reinforcement Learning: Complementary Approaches To Planning In Unknown Domains—0
Causal Bayesian Optimization—0
Studying Product Competition Using Representation Learning—0
Principal Fairness for Human and Algorithmic Decision-Making—0
On the power of conditional independence testing under model-XCode0
Counterfactual Propagation for Semi-Supervised Individual Treatment Effect Estimation—0
Constraint-Based Causal Discovery using Partial Ancestral Graphs in the presence of Cycles—0
Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates—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