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 701–750 of 1722 papers

TitleStatusHype
Double and Single Descent in Causal Inference with an Application to High-Dimensional Synthetic ControlCode0
An Efficient Doubly-Robust Test for the Kernel Treatment Effect—0
Causal Semantic Communication for Digital Twins: A Generalizable Imitation Learning Approach—0
Causal Effect Estimation with Variational AutoEncoder and the Front Door Criterion—0
Causal fault localisation in dataflow systemsCode0
Debiasing Conditional Stochastic Optimization—0
METAM: Goal-Oriented Data Discovery—0
pgmpy: A Python Toolkit for Bayesian NetworksCode4
Adjustment with Many Regressors Under Covariate-Adaptive Randomizations—0
Compositional Probabilistic and Causal Inference using Tractable Circuit ModelsCode0
Causal models in string diagrams—0
Predictive Incrementality by Experimentation (PIE) for Ad Measurement—0
Bayesian Causal Inference in Doubly Gaussian DAG-probit Models—0
Partial Identification of Causal Effects Using Proxy Variables—0
Bridging Nations: Quantifying the Role of Multilinguals in Communication on Social MediaCode0
Data AUDIT: Identifying Attribute Utility- and Detectability-Induced Bias in Task Models—0
A step towards the applicability of algorithms based on invariant causal learning on observational data—0
Matched Machine Learning: A Generalized Framework for Treatment Effect Inference With Learned Metrics—0
TSCI: two stage curvature identification for causal inference with invalid instruments—0
A Novel Two-level Causal Inference Framework for On-road Vehicle Quality Issues Diagnosis—0
One-Step Estimation of Differentiable Hilbert-Valued ParametersCode0
Synthetic Combinations: A Causal Inference Framework for Combinatorial InterventionsCode0
Differentially Private Synthetic Control—0
Don't (fully) exclude me, it's not necessary! Causal inference with semi-IVs—0
A Survey on Causal Inference for RecommendationCode1
Counterfactually Fair Regression with Double Machine Learning—0
Learning end-to-end patient representations through self-supervised covariate balancing for causal treatment effect estimationCode1
Approaching an unknown communication system by latent space exploration and causal inferenceCode0
Reliable Beamforming at Terahertz Bands: Are Causal Representations the Way Forward?—0
Application of targeted maximum likelihood estimation in public health and epidemiological studies: a systematic review—0
Bayesian Causal Forests for Multivariate Outcomes: Application to Irish Data From an International Large Scale Education Assessment—0
Inference on Optimal Dynamic Policies via Softmax ApproximationCode0
Learning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement LearningCode0
Environment Invariant Linear Least SquaresCode0
Estimating Treatment Effects from Irregular Time Series Observations with Hidden Confounders—0
PAGE: A Position-Aware Graph-Based Model for Emotion Cause Entailment in ConversationCode1
Continual Causal Inference with Incremental Observational Data—0
Hyperparameter Tuning and Model Evaluation in Causal Effect EstimationCode0
Learning high-dimensional causal effectCode0
Representation Disentaglement via Regularization by Causal Identification—0
Q-Cogni: An Integrated Causal Reinforcement Learning Framework—0
Mitigating Observation Biases in Crowdsourced Label Aggregation—0
Personalized Pricing with Invalid Instrumental Variables: Identification, Estimation, and Policy Learning—0
Variable Importance Matching for Causal InferenceCode0
Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference—0
Modular Deep Learning—0
Mental Health Coping Stories on Social Media: A Causal-Inference Study of Papageno Effect—0
Estimating Treatment Effects in Continuous Time with Hidden Confounders—0
Stochastic Online Instrumental Variable Regression: Regrets for Endogeneity and Bandit Feedback—0
Causal Inference out of Control: Estimating the Steerability of Consumption—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