arXiv research

CAN learns conditional and interventional distributions from unlabeled data.

New method disentangles mixed interventional and observational data in SEMs.

A new diffusion model encodes causal structures for better interventional sampling and edge inference.

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

In this paper, we discuss structure learning of causal networks from multiple data sets obtained by external intervention experiments where we do not know what variables are manipulated. For example, the conditions in these experiments are changed by changing temperature or using drugs, but we do not know what target v…

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…

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

Understanding the pathways whereby an intervention has an effect on an outcome is a common scientific goal. A rich body of literature provides various decompositions of the total intervention effect into pathway specific effects. Interventional direct and indirect effects provide one such decomposition. Existing estima…

New algorithm identifies best intervention without graph knowledge.

GIT uses gradient estimators to target interventions for causal discovery.

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…

Method estimates causal effects from combined interventional and observational data.

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…

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

BFTS uses Bayesian Additive Regression Trees for improved personalized mobile health interventions.

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…

Causal discovery from empirical data is a fundamental problem in many scientific domains. Observational data allows for identifiability only up to Markov equivalence class. In this paper we first propose a polynomial time algorithm for learning the exact correctly-oriented structure of the transitive reduction of any c…

Paper uses GANs to estimate effects of continuous interventions.

Study identifies components of unknown interventions in a mixture.

New method samples from any causal effect given conditional generative models.

Bayesian method for inferring causal relations from mixed observational and interventional data.

Novel approach to learn CTBNs from data with minimal interventions.

Method identifies unknown intervention targets in structural causal models from diverse data.

Synthetic Combinations learns unit-specific causal outcomes for combinatorial interventions.

Causal models bring many benefits to decision-making systems (or agents) by making them interpretable, sample-efficient, and robust to changes in the input distribution. However, spurious correlations can lead to wrong causal models and predictions. We consider the problem of inferring a causal model of a reinforcement…

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…

This paper identifies the minimal set of nodes for optimal conditional interventions in causal bandits.

Controlled interventions provide the most direct source of information for learning causal effects. In particular, a dose-response curve can be learned by varying the treatment level and observing the corresponding outcomes. However, interventions can be expensive and time-consuming. Observational data, where the treat…

We outline a representation for discrete multivariate distributions in terms of interventional potential functions that are globally normalized. This representation can be used to model the effects of interventions, and the independence properties encoded in this model can be represented as a directed graph that allows…

Extends Thompson sampling for RL with fewer episodes.

Paper tackles confounded ANMs, estimating ACEs with minimal interventions.

This study analyzes how colonial rice trade in prewar Japan affected its rice market, considering several government interventions in the two rice futures exchanges in Tokyo and Osaka. We explore the interventions in the futures markets using two procedures. First, we measure the joint degree of efficiency in the marke…

Optimal intervention in economic networks modeled as influence maximization, with hard computational problems.

New method improves mHealth user engagement using Thompson sampling for count data.

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

This work shifts focus from prediction to intervention in social systems.

Develops a framework for causal structure learning using both interventional and observational data.

We identify which latent factors change between environments in linear causal models.

Signed pairwise interactions conflate uniqueness, redundancy, and synergy

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

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

With the aim of building machine learning systems that incorporate standards of fairness and accountability, we explore explicit subgroup sample complexity bounds. The work is motivated by the observation that classifier predictions for real world datasets often demonstrate drastically different metrics, such as accura…

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

Model-agnostic interpretation techniques allow us to explain the behavior of any predictive model. Due to different notations and terminology, it is difficult to see how they are related. A unified view on these methods has been missing. We present the generalized SIPA (sampling, intervention, prediction, aggregation) …

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

Proposes DRIG for robust predictions using noise interventions.

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

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