The paper tackles matching a desired mean in causal systems through shift interventions.
problem Matching a desired mean in causal systems.
method Defining Markov equivalence classes, proposing active learning strategies, deriving lower bounds.
result Proposed active learning strategies require fewer interventions than previous approaches, especially for certain graph classes.
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.
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.
Unified Bayesian model explains in-context learning and activation steering in LLMs.
problem Understanding and controlling the behavior of large language models (LLMs) through prompts and activations.
method Developed a Bayesian model to explain and predict the effects of in-context learning and activation steering.
result Unified model predicts distinct phases and sudden shifts in LLM behavior, explaining prior empirical phenomena.
Due to the recent advancements in wearables and sensing technology, health scientists are increasingly developing mobile health (mHealth) interventions. In mHealth interventions, mobile devices are used to deliver treatment to individuals as they go about their daily lives. These treatments are generally designed to im…
New method uncovers small but significant local activities in time-series data.
problem Reconstructing small but important local activities in time-series data.
method Neural state-space models with latent causal-effect disentanglement.
result Demonstrated proof-of-concept on reconstructing ectopic foci in cardiac electrical propagation.
A-ICP selects experiments to learn causal effects efficiently.
problem Learning causal effects from observational data is difficult.
method Active learning framework based on Invariant Causal Prediction.
result Proposes intervention selection policies to reveal direct causes.
ABCI infers causal models and queries simultaneously using Bayesian active learning.
problem Inference of causal models and effects in a two-stage process is inefficient and unnatural.
method Active Bayesian Causal Inference (ABCI) using Gaussian processes for sequentially designing experiments.
result ABCI is more data-efficient and accurate in learning causal queries from fewer samples.
A new experimental design method for combinatorial interventions reduces complexity and improves accuracy.
problem Efficiently conducting all possible combinatorial interventions with multiple treatments and potential interactions.
method Probabilistic factorial experimental design, applying random combinations of treatments and adapting over multiple rounds.
result Optimal dosage of 1/2 for each treatment yields near-optimal design for estimating any k-way interaction model.
We study the problem of causal discovery through targeted interventions. Starting from few observational measurements, we follow a Bayesian active learning approach to perform those experiments which, in expectation with respect to the current model, are maximally informative about the underlying causal structure. Unli…
Bayesian nonparametric model predicts user activity and intervention success.
problem Predicting user activity and intervention success in online experiments.
method Bayesian nonparametric approach to model user heterogeneity and derive user activity predictions.
result The proposed method outperforms existing approaches in predicting user activity and intervention success.
Expands experimental design for causal discovery from limited data.
problem Challenges in causal discovery from observational and interventional data.
method Bayesian optimal experimental design incorporating recent advances in causal discovery.
result Active causal discovery of large, nonlinear SCMs with both intervention target and value selection.
GACBO optimizes unknown causal graphs with interventions.
problem Optimizing a target variable on an unknown causal graph with interventions.
method Graph Agnostic Causal Bayesian Optimisation (GACBO) seeks to balance exploitation and exploration of causal structures and functions.
result GACBO outperforms baselines in simulated and real-world applications.
Bayesian ATM improves stability and efficiency in mobile health interventions.
problem Balancing intervention efficacy with user burden in mobile health interventions.
method Bayesian extension to ATM using Kalman filter-style updates.
result Bayesian ATM achieves comparable or improved scalarized returns with lower variance and more stable policy behavior.
This work sets a universal lower bound for learning causal DAGs with atomic interventions.
problem Learning causal DAGs using only observational data results in a Markov equivalence class, requiring interventions to fully orient.
method Developed CBSP orderings and used them to prove a universal lower bound on the number of single-node interventions needed.
result The universal lower bound is within a factor of two of the minimum number of single-node interventions required to fully orient a given Markov equivalence class.
Painless Activation Steering automates post-training for LMs without manual intervention.
problem Manual post-training methods are time-consuming and labor-intensive.
method Painless Activation Steering (PAS) is a fully automated approach that requires no manual intervention.
result PAS reliably improves performance for behavior tasks but not for intelligence-oriented tasks.
We propose a method for causal inference using satellite image time series, in order to determine the treatment effects of interventions which impact climate change, such as deforestation. Simply put, the aim is to quantify the 'before versus after' effect of climate related human driven interventions, such as urbaniza…
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.
Paper proposes a new multi-task causal Gaussian process model for better prediction and uncertainty estimation.
problem Learning causal effects of interventions on different subsets of variables in a DAG.
method DAG-GP model that allows information sharing across interventions and experiments on different variables.
result DAG-GP achieves the best fitting performance and faster optimal intervention selection compared to single-task models.
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.
New algorithm learns causal structure with advice, improving efficiency.
problem Learning causal structure with side information (advice).
method Adaptive search algorithm for active causal structure learning with advice.
result Intervention cost is at most O(max{1,logψ}) times the cost for verifying the true structure, matching state-of-the-art. IUPM monitors machine learning models under gradual shifts using optimal transport and active labeling.
problem Gradual distribution shifts lead to unnoticed accuracy declines in machine learning models.
method Incremental Uncertainty-aware Performance Monitoring (IUPM) using optimal transport and active labeling.
result IUPM outperforms existing baselines in gradual shift scenarios and guides label acquisition more effectively.
We consider the problem of learning the functions computing children from parents in a Structural Causal Model once the underlying causal graph has been identified. This is in some sense the second step after causal discovery. Taking a probabilistic approach to estimating these functions, we derive a natural myopic act…
The emergence of an ageing population is a significant public health concern. This has led to an increase in the number of people living with progressive neurodegenerative disorders like dementia. Consequently, the strain this is places on health and social care services means providing 24-hour monitoring is not sustai…
Agent decides when to measure latent states in RL to improve efficiency.
problem Costly state measurement in RL negatively affects future outcomes.
method Introduces AOMDP with measurement action, uses online RL and sequential Monte Carlo.
result Reduced uncertainty improves sample efficiency and policy value.
We propose a method for learning cyclic causal models from a combination of observational and interventional equilibrium data. Novel aspects of the proposed method are its ability to work with continuous data (without assuming linearity) and to deal with feedback loops. Within the context of biochemical reactions, we a…
Percolation on complex networks has been used to study computer viruses, epidemics, and other casual processes. Here, we present conditions for the existence of a network specific, observation dependent, phase transition in the updated posterior of node states resulting from actively monitoring the network. Since tradi…
We design a self size-estimating feed-forward network (SSFN) using a joint optimization approach for estimation of number of layers, number of nodes and learning of weight matrices. The learning algorithm has a low computational complexity, preferably within few minutes using a laptop. In addition the algorithm has a l…
New insights into neural network forgetting reveal a trade-off between node activation and re-use.
problem Challenges in maintaining performance on old tasks while learning new ones.
method Theoretical analysis of synthetic and real data setups, focusing on node activation vs re-use.
result Worst forgetting occurs in an intermediate similarity regime between learned tasks.
Unified multilinear model for causal factor disentanglement.
problem Disentangling causal factors from complex data without direct manipulation.
method Hierarchical block multilinear factorization (M-mode Block SVD) and incremental approach.
result Interpretable object representation robust to occlusion and reduced training data.
ESOP uses Bayesian optimization to find optimal lock-down schedules.
problem Finding optimal lock-down schedules balancing health and economy.
method Bayesian optimization interacting with epidemiological models.
result ESOP schedules balance public health and economic impacts.
We show LLMs can be locally linear, enabling better control of activations.
problem Suboptimal control of LLM activations during generation.
method Model LLM inference as a linear dynamical system, compute feedback controllers using Jacobians, and adapt classical control theory.
result Robust, fine-grained control of LLM activations across models and tasks.
IGSD separates task-specific content channels in transformer components by comparing activation replacement with zero ablation.
problem Mechanistic interpretability of transformer components
method IGSD: paired-intervention framework for comparing activation replacement with zero ablation
result IGSD identifies an early-layer content channel in transformer components that standard importance methods underestimate.
It is widely acknowledged that addiction relapse is highly associated with spatial-temporal factors such as some specific places or time periods. Current studies suggest that those factors can be utilized for better relapse interventions, however, there is no relapse prevention application that makes use of those facto…
Hidden Markov Model helps track asymptomatic carriers in pandemic.
problem Tracking spread of asymptomatic carriers (super-spreaders) during pandemic.
method Applied Hidden Markov Model to analyze COVID-19 data.
result Better assessment of spread extent for calibrated interventions.
A fast algorithm for counting Markov equivalent DAGs and designing experiments.
problem Counting Markov equivalent DAGs and designing experiments efficiently.
method LazyIter algorithm for efficient iteration over MECs, utilizing intervention results.
result Significant reduction in time complexity for sparse graphs (O(n)).
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 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.
Privacy issues related to video camera feeds have led to a growing need for suitable alternatives that provide functionalities such as user authentication, activity classification and tracking in a noninvasive manner. Existing infrastructure makes Wi-Fi a possible candidate, yet, utilizing traditional signal processing…
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.
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…
Activity recognition is the ability to identify and recognize the action or goals of the agent. The agent can be any object or entity that performs action that has end goals. The agents can be a single agent performing the action or group of agents performing the actions or having some interaction. Human activity recog…
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…
Online active learning improves model performance in high-volume production processes.
problem Outliers degrade the predictive performance of online active learning models.
method Proposes a robust estimator and bounds the search area of a conditional D-optimal algorithm.
result The proposed method improves the performance of online active learning in the presence of outliers.
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.
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.
Causal inference is central to many areas of artificial intelligence, including complex reasoning, planning, knowledge-base construction, robotics, explanation, and fairness. An active community of researchers develops and enhances algorithms that learn causal models from data, and this work has produced a series of im…
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.