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.
DCBO optimizes interventions in evolving causal systems.
problem Optimal interventions in time-varying causal systems.
method Combines sequential decision making, causal inference, and GP emulation.
result DCBO identifies optimal interventions faster than competitors.
New algorithm reduces interventional strategy complexity for causal graph discovery.
problem Designing efficient interventional strategies for causal graph discovery.
method Developed an r-adaptive algorithm for causal graph discovery that minimizes the number of interventions.
result Achieved an approximation of O(min{r, log n} * n^{1/min{r, log n}}) for the verification number.
Unified RL survey for healthcare AI interventions.
problem Limited real-life application of RL in healthcare.
method Unified technical survey and case studies.
result Bridge between dynamic treatment regimes and mobile health.
cCBO optimizes interventions in causal graphs under constraints.
problem Finding optimal interventions in causal graphs with constraints.
method Exploits graph structure, uses Gaussian processes, and sequentially selects interventions.
result Successful trade-off between fast convergence and feasibility of interventions.
MO-CBO optimizes multiple outcomes in causal systems with minimal data.
problem Optimizing multiple outcomes in causal systems with limited data.
method Decomposes MO-CBO into multi-objective optimization tasks and uses relative hypervolume improvement for sequential intervention balancing.
result MO-CBO outperforms traditional multi-objective Bayesian optimization in causal settings.
Causal Bayesian Optimization improves global optimization with causal information.
problem Optimizing a system with causal relationships among variables.
method Combines causal inference, uncertainty quantification, and sequential decision making.
result Causal information significantly improves optimization strategies and reduces costs.
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.
AR CI framework handles complex confounders and sequential actions.
problem Low-dimensional confounders and singleton actions in causal inference.
method Sequencification to transform data into sequences, enabling CI with complex confounders and sequential actions.
result AR model can estimate multiple causal quantities using a single model, simplifying inference and improving outcome prediction.
GO-CBED optimizes experiments for specific causal queries, improving efficiency.
problem Efficiently infer causal relationships with limited resources.
method Goal-oriented Bayesian framework that maximizes expected information gain on user-specified causal quantities.
result GO-CBED outperforms existing methods in various causal tasks, especially with limited budgets.
New algorithm identifies best intervention without graph knowledge.
problem Finding best intervention in causal bandit setting with unknown graph.
method Additive combinatorial linear bandit problem with action-elimination algorithm.
result One can identify best intervention without explicitly learning graph parents.
Paper uses RL to optimize ICU load during COVID-19.
problem Optimizing ICU load during a pandemic.
method Combines epidemic model, Bayesian inference, and RL for adaptive intervention levels.
result RL policies reduce ICU burden compared to historical 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.
Framework for deferring decisions to experts in sequential medical settings.
problem Myopic and non-adaptive decision-making by ML models in sequential medical contexts.
method Sequential Learning-to-Defer (SLTD) framework using model-based reinforcement learning.
result Adaptive deferral policy improves trade-off between long-term outcomes and deferral frequency.
New method evaluates multiple social disparities using machine learning.
problem Reduction of educational disparities across multiple dimensions.
method Triply-Robust Machine Learning Approach for Causal Decomposition Analysis.
result Simultaneous interventions across multiple domains reduce disparities.
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 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.
Bandit is a framework for designing sequential experiments. In each experiment, a learner selects an arm A ∈ A A \in \mathcal{A} A ∈ A and obtains an observation corresponding to A A A . Theoretically, the tight regret lower-bound for the general bandit is polynomial with respect to the number of arms ∣ A ∣ |\mathcal{A}| ∣ A ∣ . This makes ba…
New formalism for decision making combines causal structures with MDPs, improving reinforcement learning performance.
problem Sequential decision making with causal knowledge to improve performance.
method Causal Markov Decision Processes (C-MDPs) and C-UCBVI algorithm exploiting causal structure.
result C-UCBVI achieves an i l d e O ( H S Z T ) ilde{O}(HS\sqrt{ZT}) i l d e O ( H S Z T ) regret bound, independent of actions. This paper tackles sequential distribution shifts in representation learning.
problem Learning meaningful representations in a sequence of distribution shifts.
method Nonlinear Independent Component Analysis (ICA) framework for continual causal representation learning.
result The method achieves performance comparable to joint training on multiple offline distributions and shows no benefit from the incoming new distribution on all latent variables.
Adaptive sequential testing optimizes epidemic control by learning optimal test strategies.
problem Optimizing test allocation in epidemics with network and temporal dependence.
method Adaptive sequential design with Online Super Learner for optimal test strategies.
result Superior performance in simulated university COVID-19 pandemic.
We study how to learn optimal interventions sequentially given causal information represented as a causal graph along with associated conditional distributions. Causal modeling is useful in real world problems like online advertisement where complex causal mechanisms underlie the relationship between interventions and …
Adaptive PCR improves panel data analysis with uniform guarantees.
problem Adaptive data collection in panel data settings.
method Adapting PCR to online settings using martingale concentration.
result Time-uniform guarantees for adaptive PCR in panel data.
The paper develops methods to estimate optimal treatment sequences under policy constraints.
problem Estimating the best sequence of treatments over multiple stages for individuals.
method Empirical welfare maximization approach, solving treatment assignment sequentially or simultaneously.
result Established convergence rates and upper bounds for estimation methods.
Method learns optimal treatment sequences from observational data.
problem Optimal dynamic treatment regimes for public policies and medical interventions.
method Doubly robust classification-based approach via backward induction.
result Achieves optimal convergence rate of n^(-1/2) for welfare regret.
A recommendation framework helps users choose healthcare interventions.
problem Choice overload in online healthcare communities.
method Multi-Armed Bandit (MAB) approach with innovative model components.
result Our recommendation design outperforms state-of-the-art systems.
AI improves precision health through adaptive interventions.
problem Improving healthcare through personalized and dynamic treatments.
method Reinforcement learning (RL) for adaptive interventions in digital health.
result RL shows promise in dynamic healthcare problems.
Bayesian approach learns causal concepts from diverse social surveys.
problem Inferring causal concepts from heterogeneous data with sparse changes.
method Hierarchical Bayesian model with sequential Monte Carlo sampling.
result Model infers meaningful causal concepts and plausible relations.
Q-learning with cSMART data assesses cAI tailoring variables.
problem Evaluating moderators in cAI construction.
method Clustered Q-learning with M-out-of-N Cluster Bootstrap.
result Constructs confidence intervals for causal effect moderation.
We identify and analyze selection structure in sequential data.
problem Selection biases in sequential data can distort analysis and hide underlying generation processes.
method Nonparametric identifiability of selection structure without interventional experiments.
result Selection structure is identifiable in sequential data without parametric assumptions.
The wide spread usage of automated data-driven decision support systems has raised a lot of concerns regarding accountability and fairness of the employed models in the absence of human supervision. Existing fairness-aware approaches tackle fairness as a batch learning problem and aim at learning a fair model which can…
The paper proposes a fair reinforcement learning framework to prevent healthcare disparities.
problem Unfair reinforcement learning policies in healthcare can lead to socioeconomically-disadvantaged subgroups being underprivileged.
method The paper introduces a counterfactual fairness framework and a sequential data preprocessing algorithm to achieve fair sequential decision making.
result The proposed approach greatly enhances fair access to counseling in a digital health dataset designed to reduce opioid misuse.
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.
This paper addresses robust CBs for linear SEMs with model fluctuations.
problem Designing interventions in causal systems with linear SEMs that are robust to model fluctuations.
method Develops a robust CB algorithm and analyzes its regret under model deviation.
result The proposed algorithm achieves nearly optimal i l d e O ( T ) ilde{\mathcal{O}}(\sqrt{T}) i l d e O ( T ) regret when C C C is o ( T ) o(\sqrt{T}) o ( T ) and maintains sub-linear regret for a broader range of C C C . Optimizes causal effects on unknown graphs using Causal Entropy Optimization.
problem Optimizing causal effects in unknown causal graphs.
method Causal Entropy Optimization (CEO) framework that generalizes Causal Bayesian Optimization (CBO). Incorporates causal structure uncertainty in surrogate models and intervention selection.
result CEO achieves faster convergence to global optimum compared to CBO and improves upon sequential structure learning.
We develop SHOPPER, a sequential probabilistic model of shopping data. SHOPPER uses interpretable components to model the forces that drive how a customer chooses products; in particular, we designed SHOPPER to capture how items interact with other items. We develop an efficient posterior inference algorithm to estimat…
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.
CDM models counterfactual outcomes in longitudinal data with improved accuracy.
problem Predicting counterfactual outcomes in longitudinal data with complex time-dependent confounding.
method Causal Diffusion Model (CDM) using denoising diffusion architecture with relational self-attention.
result CDM outperforms state-of-the-art methods in generating full probabilistic distributions of counterfactual outcomes.
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.
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.
Designs efficient factorial experiments for product design under budget constraints.
problem Designing effective experiments for product design with limited traffic and overlapping experiments.
method Two-stage design: first stage samples and infers performance, second stage selects a final policy.
result The method outperforms one-shot tensor completion and unstructured best-arm benchmarks.
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…
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…
New method estimates policy performance under unobserved confounding.
problem Estimating policy performance when decisions depend on unobserved variables.
method Developed worst-case bounds for robust OPE under unobserved confounding.
result Efficient procedure for computing worst-case bounds, proving statistical consistency.
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.
New setting combines state evolution and corrupted context for better decision-making.
problem Decision-making in a changing state with unreliable context.
method Proposes a new algorithm using a referee to dynamically combine contextual bandit and multi-armed bandit policies.
result Improved empirical performance compared to existing algorithms.