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 1676–1700 of 1722 papers

TitleStatusHype
Sampling-based Causal Inference in Cue Combination and its Neural Implementation—0
Real-time Top-K Predictive Query Processing over Event Streams—0
Causal Decision Trees—0
Learning from Pairwise Marginal Independencies—0
Causal inference via algebraic geometry: feasibility tests for functional causal structures with two binary observed variables—0
Removing systematic errors for exoplanet search via latent causesCode0
Distinguishing Cause from Effect Based on Exogeneity—0
Telling cause from effect in deterministic linear dynamical systems—0
Towards a Learning Theory of Cause-Effect InferenceCode0
A fast PC algorithm for high dimensional causal discovery with multi-core PCs—0
Computing Functions of Random Variables via Reproducing Kernel Hilbert Space Representations—0
Long-term causal effects via behavioral game theory—0
From dependency to causality: a machine learning approach—0
Distinguishing cause from effect using observational data: methods and benchmarks—0
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components—0
Bounding the Probability of Causation in Mediation Analysis—0
Non-linear Causal Inference using Gaussianity Measures—0
The Randomized Causation Coefficient—0
Attributes for Causal Inference in Longitudinal Observational Databases—0
Scalable Matrix-valued Kernel Learning for High-dimensional Nonlinear Multivariate Regression and Granger Causality—0
Inferring causal structure: a quantum advantage—0
Causal Inference through a Witness Protection Program—0
Beyond Bell's Theorem II: Scenarios with arbitrary causal structure—0
Counterfactual Estimation and Optimization of Click Metrics for Search Engines—0
On the Intersection Property of Conditional Independence and its Application to Causal Discovery—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