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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 76–100 of 1722 papers

TitleStatusHype
TV-SurvCaus: Dynamic Representation Balancing for Causal Survival Analysis—0
Causally Fair Node Classification on Non-IID Graph Data—0
A Unifying Framework for Robust and Efficient Inference with Unstructured Data—0
Inference for max-linear Bayesian networks with noise—0
On the Mechanistic Interpretability of Neural Networks for Causality in Bio-statisticsCode0
A Hamiltonian Higher-Order Elasticity Framework for Dynamic Diagnostics(2HOED)—0
Artificial Intelligence for Personalized Prediction of Alzheimer's Disease Progression: A Survey of Methods, Data Challenges, and Future Directions—0
Inference with few treated units—0
ReLU integral probability metric and its applications—0
Consistent Causal Inference of Group Effects in Non-Targeted Trials with Finitely Many Effect Levels—0
Causal DAG Summarization (Full Version)—0
Causality for Natural Language Processing—0
Dynamic Regularized CBDT: Variance-Calibrated Causal Boosting for Interpretable Heterogeneous Treatment Effects—0
The heterogeneous causal effects of the EU's Cohesion Fund—0
Eco-efficiency as a Catalyst for Citizen Co-production: Evidence from Chinese Cities—0
Causal-Copilot: An Autonomous Causal Analysis Agent—0
Causality-enhanced Decision-Making for Autonomous Mobile Robots in Dynamic EnvironmentsCode0
Reimagining Urban Science: Scaling Causal Inference with Large Language Models—0
On relative universality, regression operator, and conditional independence—0
A Two-Stage Interpretable Matching Framework for Causal Inference—0
Double Machine Learning for Causal Inference under Shared-State InterferenceCode0
A Framework of decision-relevant observability: Reinforcement Learning converges under relative ignorability—0
Better Decisions through the Right Causal World Model—0
Causal Inference under Interference through Designed Markets—0
Causal Inference Isn't Special: Why It's Just Another Prediction Problem—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