Causality

This paper develops novel finite-sample bounds for off-policy linear treatment effect estimation with adaptively collected data, proposing online learning algorithms to improve estimation accuracy and…

SAGD-IV: a novel functional stochastic gradient descent algorithm for stable nonparametric instrumental variable regression, excelling in handling binary outcomes and various loss functions.

This paper proposes ’natural counterfactuals’ for more realistic counterfactual reasoning in AI, using backtracking to minimize deviations from observed data while ensuring feasibility.

New algorithms conquer regret in multi-armed bandits challenged by network interference, achieving provably low regret with both known and unknown network structures.

Researchers developed a new, differentiable score function for learning causal relationships from data that reliably recovers the simplest causal model, even with complex data.

Frugal Flows: Generate realistic causal benchmarks with exact marginal causal effects, enabling robust causal method validation.

Unlocking Causal Structures: New algorithms identify latent causal relationships from interventions, even when multiple variables are affected simultaneously.

Causal bandits with unknown graphs and soft interventions are solved by establishing novel upper and lower regret bounds, plus a computationally efficient algorithm.

Researchers developed an efficient algorithm to uniquely identify causal relationships from mixed interventional and observational data with noisy interventions.

This paper introduces a novel tensor rank condition for identifying causal structures among discrete latent variables, advancing causal discovery in complex scenarios.

This paper introduces a novel framework for creating interventionally consistent surrogate models for complex simulations, addressing computational limitations and ensuring accurate policy evaluation.

This study presents CADIM, an adaptive algorithm using interventions to learn true causal relationships from mixtures of DAGs, achieving near-optimal intervention sizes and providing quantifiable opti…

Researchers uniquely estimate probabilities in Causal Bayesian Networks using simple independence assumptions, enabling analysis from observational data and simplifying counterfactual probability calc…

Unlocking identifiability of causal effects: This paper leverages functional dependencies in causal graphs to improve identifiability, leading to fewer needed variables in observational data.

PReBiM algorithm accurately estimates bi-directional causal effects from observational data, even with invalid instruments, using a novel cluster fusion approach.

This paper provides identifiability guarantees for causal disentanglement from purely observational data using nonlinear additive Gaussian noise models, addressing a major challenge in causal represen…

Hybrid causal discovery algorithm efficiently learns unique causal graphs from observational data by leveraging local substructures and topological sorting, outperforming existing methods in accuracy …

Higher-Order Causal Message Passing (HO-CMP) accurately estimates treatment effects in complex systems with unknown interference by using observed data to learn the system’s dynamics over time.

This paper presents efficient algorithms for designing cost-optimal proxy experiments to identify causal effects, significantly improving upon prior methods.

This paper introduces a novel nonparametric method to make policy evaluations from randomized trials externally valid, even when trial and target populations differ. It leverages additional covariate…