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 701–750 of 1722 papers

TitleStatusHype
CATE Lasso: Conditional Average Treatment Effect Estimation with High-Dimensional Linear Regression—0
Analyzing User Characteristics of Hate Speech Spreaders on Social Media—0
Causal Inference Using LLM-Guided Discovery—0
Counterfactual Prediction Under Selective ConfoundingCode0
Metastable Financial Markets—0
Stranger Danger! Cross-Community Interactions with Fringe Users Increase the Growth of Fringe Communities on Reddit—0
Assessing the Causal Impact of Humanitarian Aid on Food Security—0
Confounding-Robust Policy Improvement with Human-AI Teams—0
Model-Agnostic Covariate-Assisted Inference on Partially Identified Causal EffectsCode0
Understanding the Humans Behind Online Misinformation: An Observational Study Through the Lens of the COVID-19 Pandemic—0
Accurate Use of Label Dependency in Multi-Label Text Classification Through the Lens of Causality—0
Differentially Private Multi-Site Treatment Effect Estimation—0
Projecting infinite time series graphs to finite marginal graphs using number theory—0
High Dimensional Causal Inference with Variational Backdoor AdjustmentCode0
Causal Inference in Gene Regulatory Networks with GFlowNet: Towards Scalability in Large Systems—0
Causal Inference with Conditional Front-Door Adjustment and Identifiable Variational Autoencoder—0
Conditional Instrumental Variable Regression with Representation Learning for Causal Inference—0
Towards Causal Foundation Model: on Duality between Causal Inference and Attention—0
Logical Bias Learning for Object Relation Prediction—0
Algebraic and Statistical Properties of the Ordinary Least Squares InterpolatorCode0
Neural Network Parameter-optimization of Gaussian pmDAGsCode0
Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks—0
Towards Counterfactual Fairness-aware Domain Generalization in Changing Environments—0
OpportunityFinder: A Framework for Automated Causal Inference—0
Human-in-the-Loop Causal Discovery under Latent Confounding using Ancestral GFlowNets—0
Toolbox for Multimodal Learn (scikit-multimodallearn)—0
Toolbox for Multimodal Learn (scikit-multimodallearn)—0
Extracting Physical Causality from Measurements to Detect and Localize False Data Injection Attacks—0
Partially Specified Causal Simulations—0
Causal Discovery and Counterfactual Explanations for Personalized Student Learning—0
The role of causality in explainable artificial intelligence—0
Answering Causal Queries at Layer 3 with DiscoSCMs-Embracing Heterogeneity—0
MPEG: A Multi-Perspective Enhanced Graph Attention Network for Causal Emotion Entailment in ConversationsCode0
Causal inference in network experiments: regression-based analysis and design-based properties—0
MCNS: Mining Causal Natural Structures Inside Time Series via A Novel Internal Causality Scheme—0
Using causal inference to avoid fallouts in data-driven parametric analysis: a case study in the architecture, engineering, and construction industry—0
Inferring physical laws by artificial intelligence based causal models—0
Using representation balancing to learn conditional-average dose responses from clustered dataCode0
A Causal Perspective on Loan Pricing: Investigating the Impacts of Selection Bias on Identifying Bid-Response Functions—0
Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message LengthCode0
s-ID: Causal Effect Identification in a Sub-PopulationCode0
Causal Structure Recovery of Linear Dynamical Systems: An FFT based Approach—0
INTAGS: Interactive Agent-Guided Simulation—0
Measuring, Interpreting, and Improving Fairness of Algorithms using Causal Inference and Randomized Experiments—0
Emoji Promotes Developer Participation and Issue Resolution on GitHub—0
Woolf et als GWAS by subtraction is not useful for cross-generational Mendelian randomization studies—0
Federated Causal Inference from Observational DataCode0
Machine Unlearning for Causal Inference—0
Benchmarking Causal Study to Interpret Large Language Models for Source Code—0
Does Misclassifying Non-confounding Covariates as Confounders Affect the Causal Inference within the Potential Outcomes Framework?—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