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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 751–800 of 1722 papers

TitleStatusHype
Artificial Intelligence and Aesthetic Judgment—0
Active and Passive Causal Inference Learning—0
Unveiling Causalities in SAR ATR: A Causal Interventional Approach for Limited Data—0
Uplift Modeling: from Causal Inference to Personalization—0
Stable and Causal Inference for Discriminative Self-supervised Deep Visual Representations—0
Nonlinear Permuted Granger Causality—0
Towards a Causal Probabilistic Framework for Prediction, Action-Selection & Explanations for Robot Block-Stacking Tasks—0
Learning Bayesian Networks with Heterogeneous Agronomic Data Sets via Mixed-Effect Models and Hierarchical Clustering—0
A Forecaster's Review of Judea Pearl's Causality: Models, Reasoning and Inference, Second Edition, 2009—0
Generalization bound for estimating causal effects from observational network data—0
SLEM: Machine Learning for Path Modeling and Causal Inference with Super Learner Equation ModelingCode0
Diffusion Model in Causal Inference with Unmeasured ConfoundersCode0
Causal Effect Estimation on Hierarchical Spatial Graph DataCode0
Scalable Computation of Causal Bounds—0
VLUCI: Variational Learning of Unobserved Confounders for Counterfactual Inference—0
Improving the Variance of Differentially Private Randomized Experiments through Clustering—0
A continuous Structural Intervention Distance to compare Causal Graphs—0
Causal Inference for Banking Finance and Insurance A Survey—0
Learning sources of variability from high-dimensional observational studiesCode0
De-confounding Representation Learning for Counterfactual Inference on Continuous Treatment via Generative Adversarial Network—0
Multiply Robust Estimator Circumvents Hyperparameter Tuning of Neural Network Models in Causal Inference—0
Asymptotically Unbiased Synthetic Control Methods by Density Matching—0
Causality-oriented robustness: exploiting general noise interventionsCode0
Efficient Computation of Counterfactual Bounds—0
An R package for parametric estimation of causal effects—0
Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation—0
Unbiased Scene Graph Generation via Two-stage Causal Modeling—0
Root Causal Inference from Single Cell RNA Sequencing with the Negative Binomial—0
Counterfactual Explanation for Fairness in Recommendation—0
Identification of Causal Relationship between Amyloid-beta Accumulation and Alzheimer's Disease Progression via Counterfactual Inference—0
Causal inference for the expected number of recurrent events in the presence of a terminal event—0
Predictive Coding beyond Correlations—0
Towards Trustworthy Explanation: On Causal RationalizationCode0
Comparing Causal Frameworks: Potential Outcomes, Structural Models, Graphs, and Abstractions—0
Learning Conditional Instrumental Variable Representation for Causal Effect EstimationCode0
Generalized Random Forests using Fixed-Point TreesCode0
Decoding Urban-health Nexus: Interpretable Machine Learning Illuminates Cancer Prevalence based on Intertwined City Features—0
CF-GODE: Continuous-Time Causal Inference for Multi-Agent Dynamical Systems—0
FAIR: A Causal Framework for Accurately Inferring Judgments Reversals—0
Identifiable causal inference with noisy treatment and no side informationCode0
Can predictive models be used for causal inference?—0
Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confounding—0
Mitigating Cold-start Forecasting using Cold Causal Demand Forecasting Model—0
Inferring dynamic regulatory interaction graphs from time series data with perturbations—0
A Brief Review of Hypernetworks in Deep LearningCode0
Leveraging text data for causal inference using electronic health recordsCode0
A Causal Framework for Decomposing Spurious Variations—0
Task-specific experimental design for treatment effect estimation—0
Timing Process Interventions with Causal Inference and Reinforcement Learning—0
Finding Counterfactually Optimal Action Sequences in Continuous State SpacesCode0
Show:102550
← PrevPage 16 of 35Next →
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