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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 501–525 of 1722 papers

TitleStatusHype
Identification of Average Causal Effects in Confounded Additive Noise Models—0
Graph Neural Network Causal Explanation via Neural Causal ModelsCode0
Causal inference through multi-stage learning and doubly robust deep neural networks—0
An Introduction to Causal Discovery—0
A Critical Review of Causal Reasoning Benchmarks for Large Language Models—0
Identifying Macro Conditional Independencies and Macro Total Effects in Summary Causal Graphs with Latent Confounding—0
Advancements in Recommender Systems: A Comprehensive Analysis Based on Data, Algorithms, and Evaluation—0
New User Event Prediction Through the Lens of Causal Inference—0
Towards Context-Aware Emotion Recognition Debiasing from a Causal Demystification Perspective via De-confounded Training—0
Representation learning with CGAN for casual inference—0
CURLS: Causal Rule Learning for Subgroups with Significant Treatment Effect—0
Minimum Reduced-Order Models via Causal Inference—0
The Computational Curse of Big Data for Bayesian Additive Regression Trees: A Hitting Time AnalysisCode0
Credit Ratings: Heterogeneous Effect on Capital Structure—0
Compositional Models for Estimating Causal Effects—0
Causal Inference with Latent Variables: Recent Advances and Future Prospectives—0
Integrating Fuzzy Logic with Causal Inference: Enhancing the Pearl and Neyman-Rubin Methodologies—0
Probabilistic Temporal Prediction of Continuous Disease Trajectories and Treatment Effects Using Neural SDEs—0
LLMs Are Prone to Fallacies in Causal Inference—0
Standardizing Structural Causal ModelsCode0
Spillover Detection for Donor Selection in Synthetic Control Models—0
Causal Post-Processing of Predictive Models—0
Investigating potential causes of Sepsis with Bayesian network structure learning—0
Orthogonalized Estimation of Difference of Q-functions—0
Counterfactual-based Root Cause Analysis for Dynamical Systems—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