arXiv research

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

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

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

Extended LPCMCI learns causal models from interventional data to minimize prediction error.

New algorithms verify and search causal graphs with minimal interventions.

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

New algorithm reduces interventional strategy complexity for causal graph discovery.

GIT uses gradient estimators to target interventions for causal discovery.

Proposes minimal interventions over counterfactual explanations for algorithmic recourse.

Efficient algorithms learn causal graphs with minimal interventions.

Statistical test evaluates if personalizing interventions is cost-effective.

Optimizes intervention design for causal discovery using integer programming.

Novel approach to learn CTBNs from data with minimal interventions.

MO-CBO optimizes multiple outcomes in causal systems with minimal data.

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

New method finds subsets of vertices to minimize interventions in causal discovery.

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

Paper tackles confounded ANMs, estimating ACEs with minimal interventions.

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

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

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

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

PULSE estimator improves prediction in causal inference with bounded interventions.

The paper proposes a method to measure fairness through equality of effort using algorithmic recourse.

Proposes DRIG for robust predictions using noise interventions.

GANICE improves GAN-based causal inference by minimizing averaged Wasserstein risk.

The paper tackles intervention generalization using factor graph models.

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

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

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…

New classifiers ensure fairness by adjusting a base classifier's operating characteristics.

Adapts causal inference for high-dimensional treatments like text strings.

RandomNet uses random search to design neural architectures without much human intervention.

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…

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…

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

New algorithms optimize decision rules in strategic scenarios, minimizing prediction risk and incentivizing better outcomes.

This paper extends stable blanket theory to models with hidden variables and causal cycles.

New method disentangles mixed interventional and observational data in SEMs.

Signed pairwise interactions conflate uniqueness, redundancy, and synergy

Adaptive PCR improves panel data analysis with uniform guarantees.

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

Recurring international financial crises have adverse socioeconomic effects and demand novel regulatory instruments or strategies for risk management and market stabilization. However, the complex web of market interactions often impedes rational decisions that would absolutely minimize the risk. Here we show that, for…

Bayesian method for causal discovery from unknown general interventions.

Interventional data helps identify latent factors without distributional assumptions.

Scientific and business practices are increasingly resulting in large collections of randomized experiments. Analyzed together, these collections can tell us things that individual experiments in the collection cannot. We study how to learn causal relationships between variables from the kinds of collections faced by m…

IntDC framework uncovers causal relationships from non-interventional data.

Algorithm detects causal change points quickly with adaptive interventions.