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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 901–925 of 1722 papers

TitleStatusHype
Causal Inference (C-inf) -- closed form worst case typical phase transitions—0
Causal Inference (C-inf) -- asymmetric scenario of typical phase transitions—0
An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal EffectsCode0
Causally-Aware Intraoperative Imputation for Overall Survival Time Prediction—0
Layout-Based Causal Inference for Object Navigation—0
Causal Deep Learning—0
PADCLIP: Pseudo-labeling with Adaptive Debiasing in CLIP for Unsupervised Domain Adaptation—0
Identifying causal effects with subjective ordinal outcomes—0
Nuisance Function Tuning and Sample Splitting for Optimal Doubly Robust Estimation—0
Simplifying Causality: A Brief Review of Philosophical Views and Definitions with Examples from Economics, Education, Medicine, Policy, Physics and Engineering—0
Orthogonal Series Estimation for the Ratio of Conditional Expectation Functions—0
Cross-Dataset Propensity Estimation for Debiasing Recommender Systems—0
Local Policy Improvement for Recommender Systems—0
Causal Inference for Knowledge Graph based RecommendationCode0
A Layered Architecture for Universal Causality—0
On the Relationship Between Explanation and Prediction: A Causal View—0
Whole Brain Network Dynamics of Epileptic Seizures at Single Cell Resolution—0
Doubly Robust Kernel Statistics for Testing Distributional Treatment EffectsCode0
Robust detection and attribution of climate change under interventionsCode0
Neighborhood Adaptive Estimators for Causal Inference under Network Interference—0
Short-term shock, long-lasting payment: Evidence from the Lushan Earthquake—0
Evaluating Digital Agriculture Recommendations with Causal Inference—0
Logic and Commonsense-Guided Temporal Knowledge Graph CompletionCode0
Causal Inference with Conditional Instruments using Deep Generative Models—0
Causal Deep Reinforcement Learning Using Observational Data—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