CAN learns conditional and interventional distributions from unlabeled data.
problem Learning conditional and interventional distributions from unlabeled data.
method CAN framework with LGN and CIGN architectures, equipped with an intervention mechanism.
result CAN generates both interventional and conditional samples without needing the causal graph.
This paper identifies the minimal set of nodes for optimal conditional interventions in causal bandits.
problem Optimizing decision-making in causal bandits with conditional interventions.
method Graphical characterization and efficient algorithm to identify the minimal set of nodes.
result The proposed algorithm significantly prunes the search space and accelerates convergence rates.
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…
Paper relaxes faithfulness assumption for causal discovery using interventions.
problem Violation of faithfulness assumption in natural systems leads to incorrect causal structure identification.
method Use intervention-immediacy faithfulness assumption to identify causal structures with hard interventions.
result Interventions contain information about causal structure that can identify causal structures when faithfulness is violated.
Study identifies causal relationships without direct supervision from unknown interventions.
problem Identify causal relationships from unknown interventions without direct supervision.
method General nonparametric setting with multiple datasets from unknown interventions.
result Identify ground truth latents and causal graph up to ambiguities.
Introduces CStrees for modeling context-specific causal models from observational and interventional data.
problem Modeling context-specific causal relationships from mixed data types.
method Introduces CStrees with a novel factorization criterion and graphical characterization for context-specific conditional independence models.
result Derives a graphical characterization of model equivalence for observational CStrees and extends it to CStree models under context-specific interventions.
Study identifies components of unknown interventions in a mixture.
problem Identify components of a mixture of unknown interventions on a causal Bayesian Network.
method Construct example showing components not identifiable. Prove identifiability under mild conditions. Develop efficient algorithm for recovery. Analyze performance in simulation.
result Components of a mixture of unknown interventions can be uniquely identified under certain conditions.
New method identifies latent causal graphs without parametric assumptions.
problem Identifying latent causal graphs without parametric assumptions.
method Constructive proofs with new graphical concepts.
result Conditions for nonparametric identification of latent causal graphs.
A new metric compares true and learned causal graphs considering data and graph structure.
problem Comparing true and learned causal graphs accurately.
method Continuous Structural Intervention Distance (CSID) using conditional mean embeddings and maximum mean discrepancy.
result Validated the CSID with synthetic data, showing its effectiveness in comparing causal graphs.
fCBO optimizes interventions in causal graphs using Gaussian processes.
problem Optimizing interventions in known causal graphs.
method Functional causal Bayesian optimization (fCBO) using Gaussian processes and expected improvement acquisition.
result Functional interventions can lead to better target effects and optimal conditional effects.
Paper recovers latent causal structure and linear transformation from indirect observations.
problem Recovering latent causal structure and linear transformation from indirect observations.
method Established sufficient conditions for DAG recovery, leveraged score function properties, and used soft/hard interventions.
result Perfect recovery of latent DAG structure and linear transformation up to scaling using soft interventions, hard interventions with additional hypothesis testing.
The paper presents a method to estimate joint interventional distributions from marginal interventional data.
problem Estimating joint interventional distributions from marginal interventional data.
method The paper extends the Causal Maximum Entropy method to use interventional data and employs Lagrange duality to prove the solution lies in the exponential family.
result The method allows for causal feature selection and inference of joint interventional distributions.
We develop a model to predict effects of sequential interventions, clarifying their combined impact.
problem Uncertainty in generalizing behavioral predictions for combinations of interventions.
method Explicit model for composition of interventions, identifying their combined effect.
result Our compositional model aids prediction in sparse data conditions.
Interventional data helps identify latent factors without distributional assumptions.
problem Identifying latent factors from interventional data without distributional assumptions.
method Leveraging geometric signatures of latent factors' support from interventional data.
result Latent causal factors can be identified up to permutation and scaling given data from perfect do-interventions.
BaCaDI discovers causal structures from unknown interventions.
problem Inferring causal structures from unknown interventions with limited data.
method Bayesian framework with gradient-based variational inference.
result BaCaDI outperforms related methods in identifying causal structures and intervention targets.
Statistical test evaluates if personalizing interventions is cost-effective.
problem Balancing the benefits of personalizing interventions with their potential costs.
method Developed a statistical hypothesis test to assess the performance of personalized interventions.
result The test shows that personalized interventions can outperform standard approaches under certain conditions.
Game theory approach to predicting and responding to interventions based on causal relationships.
problem Optimizing predictions and interventions in response to observational data.
method Prediction-intervention game framework, focusing on invariant subsets of covariates.
result Stable-blanket predictors are optimal for certain follower objectives and under specific conditions.
Efficient algorithms learn causal graphs with minimal interventions.
problem Learning causal relationships between observed variables in the presence of latents.
method Bi-criteria approximation goal combining intervention design and graph property testing.
result Achieve intervention cost within a small constant factor of the optimal.
The paper tackles intervention generalization using factor graph models.
problem Generalizing from past experiments to novel conditions is hard due to combinatorial space of possible interventions.
method The approach is based on intervention factor models (IFMs) and factor graph models.
result Conditions for identifiability of expected outcomes of new regimes are derived.
Bayesian method optimizes interventions for causal discovery.
problem Active interventions are needed for causal discovery when observational data is insufficient.
method Bayesian optimization-based approach using observational data and pre-experimental evaluation of interventions.
result Demonstrated effectiveness through various experiments.
Study functional confounders in causal inference, enabling estimable effects.
problem Causal inference challenges with functional confounders violating positivity.
method Functional interventions, functional positivity, gradient fields, Level-set Orthogonal Descent Estimation (LODE).
result Valid causal effect estimation under certain conditions.
New approach to counterfactual reasoning avoids demographic interventions.
problem Limitations of traditional counterfactual reasoning in AI systems.
method Backtracking counterfactual approach instead of interventional.
result Allows addressing social concerns without demographic interventions.
Paper characterizes causal graphs from hard interventions and proposes a learning algorithm.
problem Discovering causal structure from hard interventions and observational data.
method Proposes graphical constraints and a learning algorithm based on do-calculus.
result Characterizes interventional equivalence classes of causal graphs with latent variables.
Simplified identification methods for causal inference with arbitrary interventional distributions.
problem Estimating cause-effect relationships from data with experimental interventions.
method Using Single World Intervention Graphs and nested model factorization, we provide algorithms for identifying causal parameters from mixed observational and interventional distributions.
result Our algorithms are complete for certain types of interventional marginal distributions.
The paper presents efficient methods for identifying causal graphs with latent variables.
problem Recovering causal graphs with latent variables while minimizing intervention costs.
method Two intervention cost models (linear and identity) are considered. Algorithms are provided for both models.
result Upper bounds on the number of interventions needed for recovery, and approximation factors for the linear cost model.
Bayesian method for inferring causal relations from mixed observational and interventional data.
problem Challenges in causal inference from heterogeneous study conditions with unknown soft interventions.
method Interventional Bayesian Gaussian equivalent score for a mixture of observational and interventional data.
result Effective characterisation of posterior distributions of causal structures and intervention effects.
Method identifies unknown intervention targets in structural causal models from diverse data.
problem Identifying unknown intervention targets in structural causal models from heterogeneous data.
method Two-phase approach: first recovers exogenous noises, second matches with endogenous variables.
result Proposed method uniquely identifies intervention targets under causal sufficiency assumption.
A new diffusion model encodes causal structures for better interventional sampling and edge inference.
problem Lack of causal analysis in standard diffusion models.
method Causality-encoded diffusion framework that trains conditional models consistent with a directed acyclic graph.
result The method enables accurate interventional sampling and edge inference, with theoretical guarantees and practical applications.
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…
The paper tackles causal disentanglement with linear models and interventions.
problem Identify latent variables in a causal model from observed data.
method Use linear transformations and interventions to uniquely identify latent variables.
result A single intervention on each latent variable is sufficient for identifying the latent causal model.
New method samples from any causal effect given conditional generative models.
problem Sampling from un/conditional interventional distributions in high-dimensional data.
method Sequence of push-forward computations of conditional generative models.
result Algorithm enables sampling from any identifiable interventional distribution.
Develops a personalized reinforcement learning algorithm for dyadic health interventions.
problem Personalizing health interventions for dyadic relationships in mobile health.
method Dyadic Reinforcement Learning (dyadic RL), a Bayesian and hierarchical online algorithm.
result Established a regret bound and demonstrated empirical performance through simulations and real data.
Clinical forecasting based on electronic medical records (EMR) can uncover the temporal correlations between patients' conditions and outcomes from sequences of longitudinal clinical measurements. In this work, we propose an intervention-augmented deep state space generative model to capture the interactions among clin…
Bayesian method estimates intervention effects in non-linear data.
problem Causal discovery from observational data with non-linear relationships.
method Gaussian Process Networks (GPN) with Bayesian estimation and Monte Carlo methods.
result Approach accurately identifies and reflects uncertainty of causal estimates.
New algorithm finds causal parent nodes without graph learning.
problem Finding causal relationships without knowing the graph structure.
method Developed an efficient algorithm using atomic interventions.
result Algorithm optimally performs interventions with sublinear complexity.
This paper tackles causal interactions in mixtures of DAGs using interventions.
problem Learning causal interactions among variables governed by a mixture of causal systems.
method Establishes necessary and sufficient conditions for intervention size, designs an adaptive algorithm.
result Identifies true edges in a mixture of DAGs using optimal or near-optimal interventions.
GnIES recovers causal structure from unknown interventions.
problem Recovering causal structure from unknown interventions.
method Characterization of equivalence classes, greedy learning algorithm GnIES.
result GnIES recovers the equivalence class of the data-generating model.
We propose a simple method to learn linear causal cyclic models in the presence of latent variables. The method relies on equilibrium data of the model recorded under a specific kind of interventions ("shift interventions"). The location and strength of these interventions do not have to be known and can be estimated f…
New findings show single-treatment effects are unidentifiable in factorial experiments.
problem Identifying the effect of a single intervention in factorial experiments.
method Formalized sufficient conditions for the identifiability of single-treatment effects and developed nonparametric sharp bounds.
result Researchers must justify assumptions for extrapolating single-treatment effects.
New bandit model for healthcare intervention planning.
problem Maximizing patient health with limited monitoring resources.
method Developed Collapsing Bandits model and derived optimal policies.
result 3-order-of-magnitude speedup in algorithm performance.
We study heterogeneity in the effect of a mindset intervention on student-level performance through an observational dataset from the National Study of Learning Mindsets (NSLM). Our analysis uses machine learning (ML) to address the following associated problems: assessing treatment group overlap and covariate balance,…
Develops a method to efficiently learn causal DAGs using directed clique trees.
problem Efficiently learning causal DAGs in the presence of large cliques.
method Decomposes DAGs into independently orientable components using directed clique trees and designs a two-phase intervention algorithm.
result Proves that the number of single-node interventions necessary to orient any DAG in an EC is at least the sum of half the size of the largest cliques in each chain component of the essential graph.
Estimates joint causal effects using single-variable interventions on nonlinear models.
problem Estimating joint causal effects from single-variable interventions.
method Identifiability result and practical estimator for decomposing causal effects.
result Joint effects can be inferred without joint interventional data for nonlinear additive models.
This paper extends stable blanket theory to models with hidden variables and causal cycles.
problem Identifying stable predictors in models with hidden variables and causal cycles.
method Use acyclic directed mixed graphs (ADMGs) and directed graphs (DGs) with m-separation and σ-separation to characterize and construct intervention-stable predictor sets. result Graphical characterizations of Markov blankets, stable frontiers, and stable blankets in models with hidden variables and cycles.
Novel approach to learn CTBNs from data with minimal interventions.
problem Learning CTBNs from time-course data with limited resources.
method Variational approximation of expected information gain for experimental design.
result Semi-analytical expression for structure and parameter learning.
Synthetic Combinations learns unit-specific causal outcomes for combinatorial interventions.
problem Estimating unit-specific causal outcomes for all combinations of p interventions in a heterogeneous setting. method Latent factor model with Fourier expansion sparsity, imposing structure across units and interventions.
result Synthetic Combinations provides consistent estimation with poly(r) * (N + s^2p) observations, outperforming previous methods.
GAMBITTS uses GenAI for adaptive interventions, improving decision-making.
problem Adaptive interventions with GenAI-generated content.
method Generator-mediated bandit-Thompson sampling (GAMBITTS).
result GAMBITTS outperforms standard bandit methods in mobile health interventions.
New method for selective prediction under interventions learns causal structure from data.
problem Tight uncertainty sets in selective conformal prediction under unknown interventional settings.
method Partial causal structure learning for descendant indicators, contamination-robust coverage theorem, algorithms for descendant discovery and distance estimation.
result Valid selective conformal prediction under contamination up to 30% with controlled coverage.