SOTAVerified

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 926–950 of 1722 papers

TitleStatusHype
The Local Approach to Causal Inference under Network Interference—0
The Logic of Counterfactuals and the Epistemology of Causal Inference—0
Causal inference and policy evaluation without a control group—0
The Noisy-Logical Distribution and its Application to Causal Inference—0
The Proximal ID Algorithm—0
The Randomized Causation Coefficient—0
The role of causality in explainable artificial intelligence—0
The Secrets of Machine Learning: Ten Things You Wish You Had Known Earlier to be More Effective at Data Analysis—0
The Short-term Impact of Congestion Taxes on Ridesourcing Demand and Traffic Congestion: Evidence from Chicago—0
The Value Added of Machine Learning to Causal Inference: Evidence from Revisited Studies—0
The wealth of nations and the health of populations: A quasi-experimental design of the impact of sovereign debt crises on child mortality—0
The worst of both worlds: A comparative analysis of errors in learning from data in psychology and machine learning—0
Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confounding—0
Time Series Treatment Effects Analysis with Always-Missing Controls—0
Timing Process Interventions with Causal Inference and Reinforcement Learning—0
Too Fast Causal Inference under Causal Insufficiency—0
Toolbox for Multimodal Learn (scikit-multimodallearn)—0
Toolbox for Multimodal Learn (scikit-multimodallearn)—0
Top-N Recommendation with Counterfactual User Preference Simulation—0
Topological Analysis of Seizure-Induced Changes in Brain Hierarchy Through Effective Connectivity—0
Toward a Theory of Causation for Interpreting Neural Code Models—0
Towards a Causal Probabilistic Framework for Prediction, Action-Selection & Explanations for Robot Block-Stacking Tasks—0
Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens—0
Towards a Science of Causal Interpretability in Deep Learning for Software Engineering—0
Towards Causal Foundation Model: on Duality between Causal Inference and Attention—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