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 376–400 of 1722 papers

TitleStatusHype
From Correlation to Causation: Understanding Climate Change through Causal Analysis and LLM Interpretations—0
Causally Consistent Normalizing Flow—0
On the Role of Surrogates in Conformal Inference of Individual Causal EffectsCode0
ABC3: Active Bayesian Causal Inference with Cohn Criteria in Randomized ExperimentsCode0
Moderating the Mediation Bootstrap for Causal Inference—0
Adaptive Nonparametric Perturbations of Parametric Bayesian ModelsCode0
Do LLMs Act as Repositories of Causal Knowledge?—0
Learning Structural Causal Models from Ordering: Identifiable Flow Models—0
Multimodal Sentiment Analysis Based on Causal Reasoning—0
Political-LLM: Large Language Models in Political Science—0
A Compositional Atlas for Algebraic Circuits—0
Estimating the treatment effect over time under general interference through deep learner integrated TMLE—0
Disentangled Representation Learning for Causal Inference with Instruments—0
Graph Disentangle Causal Model: Enhancing Causal Inference in Networked Observational Data—0
Deep Causal Inference for Point-referenced Spatial Data with Continuous TreatmentsCode0
Online Experimental Design With Estimation-Regret Trade-off Under Network Interference—0
Nature versus nurture in galaxy formation: the effect of environment on star formation with causal machine learning—0
Practical Performative Policy Learning with Strategic AgentsCode0
COLD: Causal reasOning in cLosed Daily activitiesCode0
Causal Inference in Finance: An Expertise-Driven Model for Instrument Variables Identification and Interpretation—0
OccludeNet: A Causal Journey into Mixed-View Actor-Centric Video Action Recognition under OcclusionsCode0
Double Machine Learning for Adaptive Causal Representation in High-Dimensional Data—0
Heterophilic Graph Neural Networks Optimization with Causal Message-passing—0
Generative Intervention Models for Causal Perturbation Modeling—0
Logarithmic Neyman Regret for Adaptive Estimation of the Average Treatment Effect—0
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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