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 151–200 of 1722 papers

TitleStatusHype
Rolling with the Punches: Resilient Contrastive Pre-training under Non-Stationary Drift—0
GST-UNet: Spatiotemporal Causal Inference with Time-Varying ConfoundersCode0
Optimistic Algorithms for Adaptive Estimation of the Average Treatment Effect—0
Causal Interpretations in Observational Studies: The Role of Sociocultural Backgrounds and Team Dynamics—0
Practically Effective Adjustment Variable Selection in Causal Inference—0
Heterogeneous Treatment Effect in Time-to-Event Outcomes: Harnessing Censored Data with Recursively Imputed Trees—0
Can We Validate Counterfactual Estimations in the Presence of General Network Interference?Code0
Optimizing Feature Selection in Causal Inference: A Three-Stage Computational Framework for Unbiased Estimation—0
Fixed-Population Causal Inference for Models of Equilibrium—0
PUATE: Efficient Average Treatment Effect Estimation from Treated (Positive) and Unlabeled Units—0
Targeted Data Fusion for Causal Survival Analysis Under Distribution Shift—0
Real-Time Anomaly Detection with Synthetic Anomaly Monitoring (SAM)—0
Unfaithful Probability Distributions in Binary Triple of Causality Directed Acyclic Graph—0
STGCN-LSTM for Olympic Medal Prediction: Dynamic Power Modeling and Causal Policy Optimization—0
Reconciling Predictive Multiplicity in PracticeCode0
Detecting clinician implicit biases in diagnoses using proximal causal inferenceCode0
CausalSR: Structural Causal Model-Driven Super-Resolution with Counterfactual InferenceCode0
Philip G. Wright, directed acyclic graphs, and instrumental variables—0
REX: Causal Discovery based on Machine Learning and Explainability techniquesCode1
Automatic Debiased Machine Learning for Smooth Functionals of Nonparametric M-Estimands—0
Beyond Reward Hacking: Causal Rewards for Large Language Model AlignmentCode4
Kolmogorov-Arnold Networks for Time Series Granger Causality Inference—0
ML-assisted Randomization Tests for Detecting Treatment Effects in A/B Experiments—0
MECD+: Unlocking Event-Level Causal Graph Discovery for Video ReasoningCode1
Causal Claims in Economics—0
Doubly Robust Inference on Causal Derivative Effects for Continuous TreatmentsCode0
Automatic Double Reinforcement Learning in Semiparametric Markov Decision Processes with Applications to Long-Term Causal Inference—0
Do we actually understand the impact of renewables on electricity prices? A causal inference approachCode2
Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessing—0
Explainable Federated Bayesian Causal Inference and Its Application in Advanced ManufacturingCode0
An Algorithmic Approach for Causal Health Equity: A Look at Race Differentials in Intensive Care Unit (ICU) Outcomes—0
Coarsened confounding for causal effects: a large-sample framework—0
A Point Process Model for Optimizing Repeated Personalized Action Delivery to Users—0
Guiding Treatment Strategies: The Role of Adjuvant Anti-Her2 Neu Therapy and Skin/Nipple Involvement in Local Recurrence-Free Survival in Breast Cancer Patients—0
Adventurer: Optimizing Vision Mamba Architecture Designs for Efficiency—0
An AI-powered Bayesian generative modeling approach for causal inference in observational studies—0
Decoding the Flow: CauseMotion for Emotional Causality Analysis in Long-form Conversations—0
A Graphical Approach to State Variable Selection in Off-policy Learning—0
Adventures in Demand Analysis Using AI—0
HNCI: High-Dimensional Network Causal InferenceCode0
From Correlation to Causation: Understanding Climate Change through Causal Analysis and LLM Interpretations—0
A Systems Thinking Approach to Algorithmic Fairness—0
Eliciting Causal Abilities in Large Language Models for Reasoning TasksCode1
Exploring Multi-Modal Data with Tool-Augmented LLM Agents for Precise Causal DiscoveryCode1
Causally Consistent Normalizing Flow—0
On the Role of Surrogates in Conformal Inference of Individual Causal EffectsCode0
Moderating the Mediation Bootstrap for Causal Inference—0
ABC3: Active Bayesian Causal Inference with Cohn Criteria in Randomized ExperimentsCode0
Adaptive Nonparametric Perturbations of Parametric Bayesian ModelsCode0
Do LLMs Act as Repositories of Causal Knowledge?—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