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 1051–1075 of 1722 papers

TitleStatusHype
Bimodular continuous attractor neural networks with static and moving stimuli—0
The Face of Affective Disorders—0
The fairness-accuracy landscape of neural classifiers—0
The Fundamental Limits of Structure-Agnostic Functional Estimation—0
The Hardness of Reasoning about Probabilities and Causality—0
The heterogeneous causal effects of the EU's Cohesion Fund—0
The Impact of Generative Artificial Intelligence on Market Equilibrium: Evidence from a Natural Experiment—0
The Impact of the #MeToo Movement on Language at Court -- A text-based causal inference approach—0
The Implicit Bias of Heterogeneity towards Invariance: A Study of Multi-Environment Matrix Sensing—0
The Inflation Technique for Causal Inference with Latent Variables—0
The Inflation Technique Completely Solves the Causal Compatibility Problem—0
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
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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