arXiv research

cCBO optimizes interventions in causal graphs under constraints.

Causal models help ensure fairness in systems with changing environments.

ABCI infers causal models and queries simultaneously using Bayesian active learning.

We study the problem of using causal models to improve the rate at which good interventions can be learned online in a stochastic environment. Our formalism combines multi-arm bandits and causal inference to model a novel type of bandit feedback that is not exploited by existing approaches. We propose a new algorithm t…

Directed acyclic graph (DAG) models are popular for capturing causal relationships. From observational and interventional data, a DAG model can only be determined up to its \emph{interventional Markov equivalence class} (I-MEC). We investigate the size of MECs for random DAG models generated by uniformly sampling and o…

Personalized interventions in social services, education, and healthcare leverage individual-level causal effect predictions in order to give the best treatment to each individual or to prioritize program interventions for the individuals most likely to benefit. While the sensitivity of these domains compels us to eval…

Evaluation metrics for prediction models don't fully reflect intervention impact.

New findings show single-treatment effects are unidentifiable in factorial experiments.

New method estimates treatment effects from high dimensional data.

New causal measures improve feature selection in AI models.

BayesIMP combines multiple causal graphs to estimate average treatment effects with uncertainty.

AR CI framework handles complex confounders and sequential actions.

Scientific practice typically involves repeatedly studying a system, each time trying to unravel a different perspective. In each study, the scientist may take measurements under different experimental conditions (interventions, manipulations, perturbations) and measure different sets of quantities (variables). The res…

New study finds targeting based on treatment effects outperforms risk-based targeting in social interventions.

New measures for causal entropy and information gain studied.

New formalism for decision making combines causal structures with MDPs, improving reinforcement learning performance.

GO-CBED optimizes experiments for specific causal queries, improving efficiency.

New framework estimates treatment effects in extreme data.

Frugal Flows learn complex data and infer marginal causal effects.

Estimates joint causal effects using single-variable interventions on nonlinear models.

CAFL breaks feedback loops in recommender systems using causal inference.

This paper tackles CRL for multi-node interventions, achieving identifiability guarantees.

This work tackles causal graph discovery with stochastic interventions to minimize the number of interventions.

Causal diagrams based on do intervention are useful tools to formalize, process and understand causal relationship among variables. However, the do intervention has controversial interpretation of causal questions for non-manipulable variables, and it also lacks the power to check the conditions related to counterfactu…

Adversarial CBO optimizes under interventions by adversaries and non-stationarities.

Our goal is to identify beneficial interventions from observational data. We consider interventions that are narrowly focused (impacting few covariates) and may be tailored to each individual or globally enacted over a population. For applications where harmful intervention is drastically worse than proposing no change…

Paper proposes scalable algorithm to estimate intervention targets in linear models.

New method disentangles mixed interventional and observational data in SEMs.

The paper presents a method to estimate joint interventional distributions from marginal interventional data.

Bayesian method for causal discovery from unknown general interventions.

Interventional data helps identify latent factors without distributional assumptions.

IntDC framework uncovers causal relationships from non-interventional data.

Algorithm detects causal change points quickly with adaptive interventions.

Method learns causal effects from multiple interventions in presence of unobserved confounders.

New algorithms improve causal graph discovery with adaptive interventions, even under worst-case interventional costs.

We address the problem of optimal Central Bank intervention in the exchange rate market when interventions create feedback in the rate dynamics. In particular, we extend the work done on optimal impulse control by Cadenillas and Zapatero to incorporate temporary market reactions, of random duration and level, to Bank i…

GIT uses gradient estimators to target interventions for causal discovery.

Paper relaxes faithfulness assumption for causal discovery using interventions.

FMI uses matching to mimic interventions for causal feature learning.

BaCaDI discovers causal structures from unknown interventions.

Graph-coupled causal Bayesian optimization transfers information across related interventions.

The paper tackles matching a desired mean in causal systems through shift interventions.

Study introduces a new method to estimate causal mediation with multiple mediators.

Intervention loss stabilizes GAN training and reduces mode collapse.

Model identifies causal structure from paired observational and interventional data with unknown soft interventions.

Introduces CStrees for modeling context-specific causal models from observational and interventional data.

New algorithm reduces interventional strategy complexity for causal graph discovery.

New approach to counterfactual reasoning avoids demographic interventions.