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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 926–950 of 1722 papers

TitleStatusHype
Proximal Learning for Individualized Treatment Regimes Under Unmeasured Confounding—0
PUATE: Efficient Average Treatment Effect Estimation from Treated (Positive) and Unlabeled Units—0
Combinatorial Pure Exploration of Causal Bandits—0
Towards Counterfactual Fairness-aware Domain Generalization in Changing Environments—0
Q-Cogni: An Integrated Causal Reinforcement Learning Framework—0
Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference—0
Quantifying Confounding Bias in Neuroimaging Datasets with Causal Inference—0
Quantifying Error in the Presence of Confounders for Causal Inference—0
Quantifying How Hateful Communities Radicalize Online Users—0
Quantifying the Causal Effects of Conversational Tendencies—0
Quantifying the Economic Impact of Extreme Shocks on Businesses using Human Mobility Data: a Bayesian Causal Inference Approach—0
Quantile-Optimal Policy Learning under Unmeasured Confounding—0
Quantum causal inference in the presence of hidden common causes: An entropic approach—0
Quantum Entropic Causal Inference—0
Quantum Neural Networks for Propensity Score Estimation and Survival Analysis in Observational Biomedical Studies—0
RankPL: A Qualitative Probabilistic Programming Language—0
Rarely-switching linear bandits: optimization of causal effects for the real world—0
RealCause: Realistic Causal Inference Benchmarking—0
Real-time Top-K Predictive Query Processing over Event Streams—0
Recommendations as Treatments: Debiasing Learning and Evaluation—0
Reconciling Heterogeneous Effects in Causal Inference—0
Recover Experimental Data with Selection Bias using Counterfactual Logic—0
Reducing Length Bias in Scoring Neural Machine Translation via a Causal Inference Method—0
Regression Discontinuity Designs—0
Regret Minimization for Causal Inference on Large Treatment Space—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