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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 1176–1200 of 1722 papers

TitleStatusHype
Learning Causal Relationships from Conditional Moment Restrictions by Importance Weighting—0
Automated hypothesis generation via Evolutionary Abduction—0
Connecting Data to Mechanisms with Meta Structual Causal Model—0
-Intact-VAE: Identifying and Estimating Causal Effects under Limited Overlap—0
Mutual Information Minimization Based Disentangled Learning Framework For Causal Effect Estimation—0
Causal Triple Attention Time Series Forecasting—0
An Automated Approach to Causal Inference in Discrete Settings—0
Heterogeneous Treatment Effect Estimation using machine learning for Healthcare application: tutorial and benchmark—0
Conditional Cross-Design Synthesis Estimators for Generalizability in MedicaidCode0
Temporal Inference with Finite Factored Sets—0
Causal Inference, is just Inference: A beautifully simple idea that not everyone accepts—0
Causal Inference in Non-linear Time-series using Deep Networks and Knockoff Counterfactuals—0
Achieving Counterfactual Fairness for Causal Bandit—0
Causal Inference in Network Economics—0
Asymptotic Causal Inference—0
Minimizing bias in massive multi-arm observational studies with BCAUS: balancing covariates automatically using supervision—0
A Survey of Online Hate Speech through the Causal Lens—0
Modelling Major Disease Outbreaks in the 21st Century: A Causal Approach—0
A pragmatic approach to estimating average treatment effects from EHR data: the effect of prone positioning on mechanically ventilated COVID-19 patientsCode0
Bayesian Topic Regression for Causal InferenceCode0
Projected State-action Balancing Weights for Offline Reinforcement Learning—0
Relating Graph Neural Networks to Structural Causal Models—0
Variable Selection for Causal Inference via Outcome-Adaptive Random Forest—0
Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference ApproachCode0
Optimal transport weights for causal inferenceCode0
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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