arXiv research

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

New method estimates stochastic intervention effects in decision-making domains.

The paper proposes a new stochastic intervention control model conducted in various commodity and stock markets. The essence of the phenomenon of intervention is described in accordance with current economic theory. A review of papers on intervention research has been made. A general construction of the stochastic inte…

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

This paper tackles unknown causal graphs and soft interventions, establishing regret bounds and an efficient algorithm.

Signed pairwise interactions conflate uniqueness, redundancy, and synergy

New analysis shows surprising results on adaptation speed of causal models.

GAMBITTS uses GenAI for adaptive interventions, improving decision-making.

New algorithms for efficient causal interventions with budget constraints and without constraints.

Optimizes experiment design for causal structure learning in linear models with cycles.

Paper recovers latent causal structure and linear transformation from indirect observations.

This paper tackles causal representation learning with linear and general transformations.

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…

Increasing technological sophistication and widespread use of smartphones and wearable devices provide opportunities for innovative and highly personalized health interventions. A Just-In-Time Adaptive Intervention (JITAI) uses real-time data collection and communication capabilities of modern mobile devices to deliver…

Estimates outcomes under hypothetical scenarios using a flexible framework.

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

This paper is concerned with the problem of stochastic control of gene regulatory networks (GRNs) observed indirectly through noisy measurements and with uncertainty in the intervention inputs. The partial observability of the gene states and uncertainty in the intervention process are accounted for by modeling GRNs us…

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…

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…

The gold standard for discovering causal relations is by means of experimentation. Over the last decades, alternative methods have been proposed that can infer causal relations between variables from certain statistical patterns in purely observational data. We introduce Joint Causal Inference (JCI), a novel approach t…

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

New method disentangles mixed interventional and observational data in SEMs.

SAGD uses Langevin algorithm for efficient gradient descent.

Consider the problem of a central bank that wants to manage the exchange rate between its domestic currency and a foreign one. The central bank can purchase and sell the foreign currency, and each intervention on the exchange market leads to a proportional cost whose instantaneous marginal value depends on the current …

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.

Dynamical systems are widely used in science and engineering to model systems consisting of several interacting components. Often, they can be given a causal interpretation in the sense that they not only model the evolution of the states of the system's components over time, but also describe how their evolution is af…

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…

In a wide variety of applications, humans interact with a complex environment by means of asynchronous stochastic discrete events in continuous time. Can we design online interventions that will help humans achieve certain goals in such asynchronous setting? In this paper, we address the above problem from the perspect…

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.

This paper introduces a new class of Dynkin games, where the two players are allowed to make their stopping decisions at a sequence of exogenous Poisson arrival times. The value function and the associated optimal stopping strategy are characterized by the solution of a backward stochastic differential equation. The pa…

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.

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.

PLIs improve classifier performance by fine-tuning latent representations.

New approach to counterfactual reasoning avoids demographic interventions.

We establish causal semantics for SDEs and develop methods to reason about them.