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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 751–775 of 1722 papers

TitleStatusHype
A Topological Perspective on Causal Inference—0
A Graphical Approach to State Variable Selection in Off-policy Learning—0
Causal Inference in Geoscience and Remote Sensing from Observational Data—0
Effect of secular trend in drug effectiveness study in real world data—0
Causal Inference in Gene Regulatory Networks with GFlowNet: Towards Scalability in Large Systems—0
Causal Inference in Finance: An Expertise-Driven Model for Instrument Variables Identification and Interpretation—0
Early Identification of Pathogenic Social Media Accounts—0
Causal Inference in Educational Systems: A Graphical Modeling Approach—0
INTAGS: Interactive Agent-Guided Simulation—0
A General Framework for Treatment Effect Estimation in Semi-Supervised and High Dimensional Settings—0
A continuous Structural Intervention Distance to compare Causal Graphs—0
A Causal Adjustment Module for Debiasing Scene Graph Generation—0
A Fast Kernel-based Conditional Independence test with Application to Causal Discovery—0
A Multi-class Ride-hailing Service Subsidy System Utilizing Deep Causal Networks—0
Dynamic Survival Transformers for Causal Inference with Electronic Health Records—0
Causal inference in drug discovery and development—0
DynamicRouteGPT: A Real-Time Multi-Vehicle Dynamic Navigation Framework Based on Large Language Models—0
Eco-efficiency as a Catalyst for Citizen Co-production: Evidence from Chinese Cities—0
Economic Causal Inference Based on DML Framework: Python Implementation of Binary and Continuous Treatment Variables—0
Educational Effects in Mathematics: Conditional Average Treatment Effect depending on the Number of Treatments—0
Dynamic Regularized CBDT: Variance-Calibrated Causal Boosting for Interpretable Heterogeneous Treatment Effects—0
Causal Inference in Disease Spread across a Heterogeneous Social System—0
A Systems Thinking Approach to Algorithmic Fairness—0
Dynamical causality under invisible confounders—0
A scoping review of causal methods enabling predictions under hypothetical interventions—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