Bayesian method for causal discovery from unknown general interventions.
problem Learning causal DAGs from unknown interventions that modify parent sets.
method Bayesian approach with MCMC for approximating posterior DAGs and intervention targets.
result Bayesian method can identify DAGs and intervention targets up to equivalence classes.
This paper tackles CRL for multi-node interventions, achieving identifiability guarantees.
problem CRL under unknown multi-node interventions, focusing on single-node assumptions.
method Establishes identifiability results for general latent causal models under stochastic interventions.
result Identifiability up to ancestors using soft interventions, perfect identifiability using hard interventions.
Intervention loss stabilizes GAN training and reduces mode collapse.
problem Stabilizing GAN training and mitigating mode collapse.
method Intervention loss regularization term introduced into GAN objective.
result Improved GAN training stability and reduced mode collapse.
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 method disentangles mixed interventional and observational data in SEMs.
problem Learning causal relationships from mixed interventional and observational data.
method Developed a method to disentangle mixed interventional and observational data in linear SEMs with Gaussian noise.
result The method can identify causal graphs up to their interventional Markov Equivalence Class.
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.
New method identifies causal relationships from interventions in complex systems.
problem Learning causal representations from unknown, latent interventions with general nonlinear mixing.
method Strong identifiability results with unknown single-node interventions, using geometric structure of transformed data.
result First instance of causal identifiability from non-paired interventions for deep neural network embeddings.
ADIGen: Automatic, Debiased, and Invariant Counterfactual Generation
problem Generative models for counterfactual outcomes
method ADIGen combines Riesz regression, causal invariance, and orthogonal statistical learning
result ADIGen controls counterfactual risk under general interventions
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.
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…
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.
Model identifies causal structure from paired observational and interventional data with unknown soft interventions.
problem Identifying causal structure from observational and interventional data with unknown soft interventions.
method Proposes a scalable causal discovery model that aggregates subset-level PDAGs and applies contrastive cross-regime orientation rules.
result The model asymptotically recovers the identifiable PDAG and can orient additional edges compared to non-contrastive subset-restricted methods.
New algorithms improve causal graph discovery with adaptive interventions, even under worst-case interventional costs.
problem Discover causal relationships from data with adaptive interventions and node-dependent costs.
method Define new benchmarks and provide adaptive search algorithms for causal graph discovery.
result Logarithmic approximations achieved under various settings: atomic, bounded size interventions and generalized cost objectives.
FMI uses matching to mimic interventions for causal feature learning.
problem Challenges in causal discovery from observational data.
method Feature Matching Intervention (FMI) using matching to emulate perfect interventions.
result FMI outperforms in identifying causal features from observational data.
HealthSyn generates synthetic user behavior data for health interventions.
problem Lack of representative data for testing AI health interventions.
method Uses Markov processes to simulate diverse user actions, generating logs for ML algorithms.
result Synthetic data can be used to develop, test, and evaluate ML algorithms and RL-based interventions.
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.
New algorithms verify and search causal graphs with minimal interventions.
problem Recovering causal graphs from interventional data.
method Characterization of minimal intervention sets for verification, and adaptive graph separator algorithm for search.
result First provable algorithms for efficient verification and search of causal graphs.
Method learns causal effects from multiple interventions in presence of unobserved confounders.
problem Disentangling causal effects from sets of interventions in the presence of unobserved confounders.
method Non-linear structural causal models with additive, multivariate Gaussian noise; algorithm that learns causal model parameters by pooling data from different regimes and maximizing combined likelihood.
result Identification proofs demonstrate that causal effects of single interventions can be learned from sets of interventions, even with unobserved confounders.
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.
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.
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.
While much attention has been given to the problem of estimating the effect of discrete interventions from observational data, relatively little work has been done in the setting of continuous-valued interventions, such as treatments associated with a dosage parameter. In this paper, we tackle this problem by building …
Study develops method for estimating causal effects in continuous variables.
problem Lack of methods for estimating causal effects in continuous variables.
method Develops a method independent of data generating models for continuous variable interventions.
result Preserves identifiability of data and applies to any generating models.
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.
The paper tackles causal bandits with unknown SCMs and soft interventions, providing upper and lower bounds on regret.
problem Optimizing interventions in a causal system with unknown SCMs and soft interventions.
method Assumes unknown SCMs from a general class, allows infinite interventions, and provides upper and lower bounds on regret.
result General upper and lower bounds on cumulative achievable regret for various SCMs.
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.
New method identifies causal structures with fewer interventions.
problem Discovering causal structures from data.
method Active Intervention Targeting (AIT) method for learning causal DAGs.
result Significantly reduces the number of required interventions.
Localized Multidirectional Correction improves non-refusal target-response behavior in foundation models.
problem Controlled post-training refusal suppression in routed MoE and hybrid-MoE foundation models.
method Introduce Localized Multidirectional Correction (LoMC), a support-gated intervention framework.
result Substantially improves non-refusal target-response behavior while maintaining general capability under a compact intervention footprint.
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.
This work tackles causal graph discovery with stochastic interventions to minimize the number of interventions.
problem Discovering the true causal graph from observational data with limited interventions.
method Proposes a stochastic intervention model and studies verification and search problems with approximation algorithms.
result Provides approximation algorithms with competitive ratios for verification and search problems.
New algorithms for efficient causal interventions with budget constraints and without constraints.
problem Efficiently learning best interventions in causal graphs with budget constraints.
method Developed algorithms for both budgeted and non-budgeted causal bandits, optimizing regret and side-information usage.
result Proposed algorithms minimize cumulative regret and perform better than standard methods.
Paper tackles intervention extrapolation using identifiable representations.
problem Predicting effects of unseen interventions on outcomes.
method Combines identifiable representation learning with autoencoders to enforce linear invariance.
result Identifiable representations enable non-linear extrapolation of interventions.
POSCMs extend SCMs for causal modeling with latent contexts.
problem Causal modeling with latent contexts and endogenous mechanisms.
method Kolmogorov-Arnold-Sprecher edge-functional decomposition for explicit parametrization.
result Identifiability of structure and mechanisms under latent context.
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…
Study efficient algorithms for identifying minimum interventional sets to learn causal relationships.
problem Identify the smallest set of interventions to learn causal relationships between a subset of edges.
method Develop algorithms for subset verification and search problems under assumptions of faithfulness, causal sufficiency, and ideal interventions.
result For subset verification, an efficient algorithm is provided to compute a minimum sized interventional set.
Paper proposes scalable algorithm to estimate intervention targets in linear models.
problem Estimating intervention targets in linear models from observational and interventional data.
method The paper proposes a scalable algorithm that estimates intervention sites from the difference between precision matrices of observational and interventional datasets.
result The algorithm consistently identifies all intervention targets and updates observational Markov equivalence classes to interventional ones.
Paper establishes identifiability and achievability for causal representation learning.
problem Identifying and recovering latent causal models and variables from observational and interventional data.
method Establishes identifiability and achievability using uncoupled interventions and a recovery algorithm.
result Guaranteed perfect recovery of latent causal model and variables under uncoupled interventions.
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.
New method identifies causal variables from multi-node interventions, expanding on previous single-node approaches.
problem Inferring high-level causal variables from low-level observations under multiple interventions.
method Exploits variance trace of ground truth causal variables and regularizes for sparsity.
result First identifiability result for causal representation learning with multiple node interventions.
New framework identifies causal models with arbitrary interventions, improving realism.
problem Identify causal models with realistic interventions.
method Theoretical framework for identifying causal models with arbitrary interventions.
result Identify causal models with arbitrary interventions, up to a higher-level abstraction.
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.
New method predicts unobserved interactions between sets of elements.
problem Limited access to interactions between sets of elements.
method Generalized Synthetic Interventions (GSI) estimator.
result GSI estimator outperforms existing methods on synthetic and real data.
Estimates effects of multiple interventions with hidden confounders using single-variable interventions.
problem Estimating effects of multiple interventions in the presence of hidden confounders.
method Identifiability under nonlinear structural causal model with additive Gaussian noise; pooling and joint likelihood maximization.
result Proven identifiability and superior performance compared to baseline.
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.
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.
Extended LPCMCI learns causal models from interventional data to minimize prediction error.
problem Optimizing prediction of target variables using causal models.
method Combining observational and interventional causal discovery methods.
result Extended LPCMCI allows 60.9% optimal prediction of target variables compared to 53.6% with original LPCMCI.
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.
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.