A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Bayesian networks learn sub-population differences from data.
This work shifts focus from prediction to intervention in social systems.
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…
Unified framework for estimating indirect effects in observational studies with unmeasured confounding.
In a wide variety of applications, including personalization, we want to measure the difference in outcome due to an intervention and thus have to deal with counterfactual inference. The feedback from a customer in any of these situations is only 'bandit feedback' - that is, a partial feedback based on whether we chose…
With the aim of building machine learning systems that incorporate standards of fairness and accountability, we explore explicit subgroup sample complexity bounds. The work is motivated by the observation that classifier predictions for real world datasets often demonstrate drastically different metrics, such as accura…
Study uses PPLs to model and forecast COVID-19 spread and policy interventions.
Paper tackles estimating individual treatment effects from observational data.
A-ICP selects experiments to learn causal effects efficiently.
The method of covariate adjustment is often used for estimation of population average treatment effects in observational studies. Graphical rules for determining all valid covariate adjustment sets from an assumed causal graphical model are well known. Restricting attention to causal linear models, a recent article der…
Study finds incorporating fairness in healthcare models doesn't improve performance or net benefit.
Feature selection predicts immune state changes in RA mouse model.
We present new methods to estimate causal effects retrospectively from micro data with the assistance of a machine learning ensemble. This approach overcomes two important limitations in conventional methods like regression modeling or matching: (i) ambiguity about the pertinent retrospective counterfactuals and (ii) p…
Objective: Predict patient-specific vitals deemed medically acceptable for discharge from a pediatric intensive care unit (ICU). Design: The means of each patient's hr, sbp and dbp measurements between their medical and physical discharge from the ICU were computed as a proxy for their physiologically acceptable state …
New algorithm balances personalization and statistical validity in MRTs.
Holdout set improves risk score accuracy without biasing predictions.
Computers are increasingly used to make decisions that have significant impact in people's lives. Often, these predictions can affect different population subgroups disproportionately. As a result, the issue of fairness has received much recent interest, and a number of fairness-enhanced classifiers and predictors have…
Early recognition of risky trajectories during an Intensive Care Unit (ICU) stay is one of the key steps towards improving patient survival. Learning trajectories from physiological signals continuously measured during an ICU stay requires learning time-series features that are robust and discriminative across diverse …
The study quantifies and compares aleatoric and epistemic discrimination in ML models.
Observational cohort studies with oversampled exposed subjects are typically implemented to understand the causal effect of a rare exposure. Because the distribution of exposed subjects in the sample differs from the source population, estimation of a propensity score function (i.e., probability of exposure given basel…
AI improves precision health through adaptive interventions.
Study finds real-world datasets contain natural experiments that can improve model performance.
The paper identifies the best treatment to maximize NDPO, a key outcome in causal mediation analysis.
The opioid epidemic in the United States claims over 40,000 lives per year, and it is estimated that well over two million Americans have an opioid use disorder. Over-prescription and misuse of prescription opioids play an important role in the epidemic. Individuals who are prescribed opioids, and who are diagnosed wit…
Proposes a resampling method to compare uplift models with uncertainty.
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…
Diabetes is a major public health problem in the United States, affecting roughly 30 million people. Diabetes complications, along with the mental health comorbidities that often co-occur with them, are major drivers of high healthcare costs, poor outcomes, and reduced treatment adherence in diabetes. Here, we evaluate…
Estimates joint causal effects using single-variable interventions on nonlinear models.
Although there are millions of transgender people in the world, a lack of information exists about their health issues. This issue has consequences for the medical field, which only has a nascent understanding of how to identify and meet this population's health-related needs. Social media sites like Twitter provide ne…
This paper tackles CRL for multi-node interventions, achieving identifiability guarantees.
We consider the problem of learning causal directed acyclic graphs from an observational joint distribution. One can use these graphs to predict the outcome of interventional experiments, from which data are often not available. We show that if the observational distribution follows a structural equation model with an …
This work tackles causal graph discovery with stochastic interventions to minimize the number of interventions.
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 uses data augmentation to improve causal effect estimation.
Paper proposes scalable algorithm to estimate intervention targets in linear models.
It is not clear how to target patients who are most likely to benefit from digital care management programs ex-ante, a shortcoming of current risk score based approaches. This study focuses on defining impactability by identifying those patients most likely to benefit from technology enabled care management, delivered …
EGDL predicts TB outbreaks with deep learning, integrating epidemiological models.
New method disentangles mixed interventional and observational data in SEMs.
We study the problem of estimating the continuous response over time to interventions using observational time series---a retrospective dataset where the policy by which the data are generated is unknown to the learner. We are motivated by applications where response varies by individuals and therefore, estimating resp…
The paper presents a method to estimate joint interventional distributions from marginal interventional data.
Bayesian method for causal discovery from unknown general interventions.
Interventional data helps identify latent factors without distributional assumptions.
IntDC framework uncovers causal relationships from non-interventional data.
Algorithm detects causal change points quickly with adaptive interventions.
Deep learning using neural networks has provided advances in image style transfer, merging the content of one image (e.g., a photo) with the style of another (e.g., a painting). Our research shows this concept can be extended to analyse the design of streetscapes in relation to health and wellbeing outcomes. An Austral…
Method learns causal effects from multiple interventions in presence of unobserved confounders.
New algorithms improve causal graph discovery with adaptive interventions, even under worst-case interventional costs.
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…