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 701–725 of 1722 papers

TitleStatusHype
Differentially Private Covariate Balancing Causal Inference—0
Differentially Private Joint Independence Test—0
Differentially Private Multi-Site Treatment Effect Estimation—0
Differentially Private Synthetic Control—0
Complementary Advantages of ChatGPTs and Human Readers in Reasoning: Evidence from English Text Reading Comprehension—0
Causal inference approach to appraise long-term effects of maintenance policy on functional performance of asphalt pavements—0
On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges—0
A step towards the applicability of algorithms based on invariant causal learning on observational data—0
Directed Acyclic Graph Convolutional Networks—0
Causal Inference Based Single-branch Ensemble Trees For Uplift Modeling—0
Causal Analysis and Classification of Traffic Crash Injury Severity Using Machine Learning Algorithms—0
Discovering Context Specific Causal Relationships—0
Disentangled Latent Representation Learning for Tackling the Confounding M-Bias Problem in Causal Inference—0
Disentangled Representation Learning for Causal Inference with Instruments—0
Causal Inference by Stochastic Complexity—0
Comparing Causal Frameworks: Potential Outcomes, Structural Models, Graphs, and Abstractions—0
Adjusting for Confounders with Text: Challenges and an Empirical Evaluation Framework for Causal Inference—0
Comment on "Blessings of Multiple Causes"—0
Combining Offline Causal Inference and Online Bandit Learning for Data Driven Decision—0
Distinguishing cause from effect using observational data: methods and benchmarks—0
Distributionally Robust Causal Inference with Observational Data—0
Robust Multi-instance Learning with Stable Instances—0
A novel approach to the relationships between data features -- based on comprehensive examination of mathematical, technological, and causal methodology—0
Omitted Labels Induce Nontransitive Paradoxes in Causality—0
A Causal Lens for Controllable Text Generation—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