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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 801–825 of 1722 papers

TitleStatusHype
Robust detection and attribution of climate change under interventionsCode0
CausalEGM: a general causal inference framework by encoding generative modelingCode1
On Root Cause Localization and Anomaly Mitigation through Causal InferenceCode1
Neighborhood Adaptive Estimators for Causal Inference under Network Interference—0
Causal Inference via Style Transfer for Out-of-distribution GeneralisationCode1
Short-term shock, long-lasting payment: Evidence from the Lushan Earthquake—0
Logic and Commonsense-Guided Temporal Knowledge Graph CompletionCode0
Evaluating Digital Agriculture Recommendations with Causal Inference—0
Causal Inference with Conditional Instruments using Deep Generative Models—0
Causal Deep Reinforcement Learning Using Observational Data—0
Antibiotic-dependent instability of homeostatic plasticity for growth and environmental load—0
Direct-Effect Risk Minimization for Domain GeneralizationCode0
Causal Fairness Assessment of Treatment Allocation with Electronic Health Records—0
Applications of statistical causal inference in software engineering—0
Explainable Artificial Intelligence and Causal Inference based ATM Fraud Detection—0
On the Role of the Zero Conditional Mean Assumption for Causal Inference in Linear Models—0
Mitigating Frequency Bias in Next-Basket Recommendation via Deconfounders—0
Graph Neural Networks for Causal Inference Under Network Confounding—0
Realization of Causal Representation Learning to Adjust Confounding Bias in Latent SpaceCode0
Weighted Sum-Rate Maximization With Causal Inference for Latent Interference EstimationCode0
NESTER: An Adaptive Neurosymbolic Method for Causal Effect Estimation—0
Deep Causal Learning: Representation, Discovery and Inference—0
Evaluating Digital Tools for Sustainable Agriculture using Causal Inference—0
Empirical Analysis of Model Selection for Heterogeneous Causal Effect EstimationCode1
A Bayesian Semiparametric Method For Estimating Causal Quantile Effects—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