arXiv research

This work sets a universal lower bound for learning causal DAGs with atomic interventions.

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

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

The paper tackles causal bandits with unknown SCMs and soft interventions, providing upper and lower bounds on regret.

New algorithm reduces interventional strategy complexity for causal graph discovery.

ADIGen: Automatic, Debiased, and Invariant Counterfactual Generation

New algorithms verify and search causal graphs with minimal interventions.

The paper presents efficient methods for identifying causal graphs with latent variables.

Bandit is a framework for designing sequential experiments. In each experiment, a learner selects an arm $A \in \mathcal{A}$ and obtains an observation corresponding to $A$. Theoretically, the tight regret lower-bound for the general bandit is polynomial with respect to the number of arms $|\mathcal{A}|$. This makes ba…

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

We consider testing and learning problems on causal Bayesian networks as defined by Pearl (Pearl, 2009). Given a causal Bayesian network $\mathcal{M}$ on a graph with $n$ discrete variables and bounded in-degree and bounded `confounded components', we show that $O(\log n)$ interventions on an unknown causal Bayesian ne…

Game theory approach to predicting and responding to interventions based on causal relationships.

Develops a method to efficiently learn causal DAGs using directed clique trees.

New algorithm finds causal parent nodes without graph learning.

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

This paper tackles causal interactions in mixtures of DAGs using interventions.

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…

We consider the problem of learning causal networks with interventions, when each intervention is limited in size under Pearl's Structural Equation Model with independent errors (SEM-IE). The objective is to minimize the number of experiments to discover the causal directions of all the edges in a causal graph. Previou…

PULSE estimator improves prediction in causal inference with bounded interventions.

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

We study the problem of efficiently estimating the effect of an intervention on a single variable (atomic interventions) using observational samples in a causal Bayesian network. Our goal is to give algorithms that are efficient in both time and sample complexity in a non-parametric setting. Tian and Pearl (AAAI `02) h…

New method for selective prediction under interventions learns causal structure from data.

Study efficient algorithms for identifying minimum interventional sets to learn causal relationships.

Develops a personalized reinforcement learning algorithm for dyadic health interventions.

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…

Motivated by applications in computational advertising and systems biology, we consider the problem of identifying the best out of several possible soft interventions at a source node $V$ in an acyclic causal directed graph, to maximize the expected value of a target node $Y$ (located downstream of $V$). Our setting im…

Proposes a method to create robust linear models with noisy proxies of unobserved variables.

A new experimental design method for combinatorial interventions reduces complexity and improves accuracy.

The study quantifies the information needed for causal queries at different levels of Pearl's hierarchy.

RoME optimizes mobile health interventions by modeling user and time-specific effects.

Modern longitudinal studies collect feature data at many timepoints, often of the same order of sample size. Such studies are typically affected by {dropout} and positivity violations. We tackle these problems by generalizing effects of recent incremental interventions (which shift propensity scores rather than set tre…

We study how to learn optimal interventions sequentially given causal information represented as a causal graph along with associated conditional distributions. Causal modeling is useful in real world problems like online advertisement where complex causal mechanisms underlie the relationship between interventions and …

New algorithm identifies best intervention without graph knowledge.

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

Method estimates causal effects from combined interventional and observational data.

The paper shows how to learn causal representations with few environments and finite samples.

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

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…

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

The paper tackles causal bandits for SEMs, proposing algorithms that avoid estimating $2^N$ reward distributions.

Study functional confounders in causal inference, enabling estimable effects.

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

Efficiently infers interventional distributions from observational data.

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

The paper tackles generalization in machine learning by finding invariant representations of data.

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

Unified framework for estimating indirect effects in observational studies with unmeasured confounding.

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…