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
Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands—0
Causal Inference under Interference through Designed Markets—0
Causal Inference Under Interference And Network Uncertainty—0
Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition—0
ALCM: Autonomous LLM-Augmented Causal Discovery Framework—0
Causal Inference under Data Restrictions—0
Causal Inference Under Approximate Neighborhood Interference—0
Automated hypothesis generation via Evolutionary Abduction—0
Causal Inference Tools for a Better Evaluation of Machine Learning—0
A Layered Architecture for Universal Causality—0
Acquiring and Generalizing Causal Inference Rules from Deverbal Noun Constructions—0
A Causal Framework for Decomposing Spurious Variations—0
A Generative Framework for Causal Estimation via Importance-Weighted Diffusion Distillation—0
Estimation and Inference on Nonlinear and Heterogeneous Effects—0
Causal inference through multi-stage learning and doubly robust deep neural networks—0
Causal Inference through a Witness Protection Program—0
Causal Inference Struggles with Agency on Online Platforms—0
Real-Time Anomaly Detection with Synthetic Anomaly Monitoring (SAM)—0
Causal Inference out of Control: Estimating the Steerability of Consumption—0
A Unifying Framework for Robust and Efficient Inference with Unstructured Data—0
Causal Explanations for Sequential Decision Making Under Uncertainty—0
Causal Inference on Time Series using Restricted Structural Equation Models—0
Causal Inference on Outcomes Learned from Text—0
A unifying approach for doubly-robust _1 regularized estimation of causal contrasts—0
Causal Inference on Multivariate and Mixed-Type Data—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