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…
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48 results for “intervention impact”
FairPrep: Promoting Data to a First-Class Citizen in Studies on Fairness-Enhancing Interventionscs.LG
The importance of incorporating ethics and legal compliance into machine-assisted decision-making is broadly recognized. Further, several lines of recent work have argued that critical opportunities for improving data quality and representativeness, controlling for bias, and allowing humans to oversee and impact comput…
Personalized interventions in social services, education, and healthcare leverage individual-level causal effect predictions in order to give the best treatment to each individual or to prioritize program interventions for the individuals most likely to benefit. While the sensitivity of these domains compels us to eval…
Study shows SEC crypto classification led to significant market reactions.
problem Impact of SEC classification of crypto assets as securities.
method Event study methodology focusing on explicitly named crypto assets.
result Significant adverse market reactions, with returns plummeting 12% over one week.
Study uses PPLs to model and forecast COVID-19 spread and policy interventions.
problem Forecast and control the spread of COVID-19 using policy interventions.
method Compartmental models, probabilistic programming, inference, greedy algorithm.
result Optimal series of policy interventions can control the infected population.
This work shifts focus from prediction to intervention in social systems.
problem The limitations of focusing solely on prediction in automated decision systems.
method Shift from prediction-focused paradigm to intervention-oriented approach.
result A new perspective unifies statistical frameworks and tools for ADS design, implementation, and evaluation.
Evaluation metrics for prediction models don't fully reflect intervention impact.
problem Standard metrics don't accurately reflect reduction in patient outcomes from model use.
method Synthesized and discussed various evaluation methods, analyzed with simulated and real data.
result Evaluations without interventional data are limited or require strong assumptions.
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 …
Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confoundersstat.ML
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.
Aggregated variables can mask causal effects, turning unconfounded into confounded relations.
problem Aggregated variables can mask causal effects, leading to paradoxical confounding.
method Analysis of how aggregated variables can change the definition of causality and the feasibility of causal relations.
result Macro causal relations are defined by micro states, not just aggregated variables.
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.
New framework infers causal shifts in event sequences under out-of-domain interventions.
problem Inferring causal relationships in event sequences without considering out-of-domain interventions.
method Proposes a new causal framework to define ATE, designs an unbiased ATE estimator, and uses a Transformer-based neural network model.
result Demonstrates superior performance in ATE estimation and goodness-of-fit under out-of-domain-augmented point processes.
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.
New model analyzes customer churn with tensor completion and binary data.
problem Analyzing the impact of interventions on customer churn.
method Tensorized latent factor block hazard model with 1-bit tensor completion.
result Effective categorization of interventions by similar impacts.
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…
CPP improves predictive model outputs for better intervention decisions.
problem Predictive models often misguide resource allocation.
method Causal post-processing techniques using limited experimental data.
result CPP can improve intervention decisions, especially with imperfect causal signals.
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…
Robustly detects and attributes climate change impacts under interventions.
problem Detect and attribute climate change impacts from observations robustly.
method Supervised learning with anchor regression for robust predictions under interventions.
result CO2 forcing can be robustly predicted from temperature patterns under strong solar forcing interventions.
Modeling government intervention's impact on company market technology growth.
problem Understanding how governments influence technology growth and innovation diffusion.
method Proposed a simple extension of TGID models, incorporating a government intervention parameter.
result High government intervention can destabilize market development, lowering technology levels.
New method explains ML performance gaps without causal knowledge.
problem Understanding why ML algorithms perform differently across domains.
method Nonparametric hierarchical decomposition framework.
result Detailed variable-level explanations for performance gaps.
Reserve-Dependent Surrenderq-fin.MF
We study the modelling and valuation of surrender and other behavioural options in life insurance and pension. We place ourselves in between the two extremes of completely arbitrary intervention and optimal intervention by the policyholder. We present a method that is based on differential equations and that can be use…
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…
We solve the dynamics of large spherical Minority Games (MG) in the presence of non-negligible time dependent external contributions to the overall market bid. The latter represent the actions of market regulators, or other major natural or political events that impact on the market. In contrast to non-spherical MGs, t…
Study uses causal machine learning to assess coupon campaign impact on retailer sales.
problem Assessing the causal effect of a coupon campaign on retailer sales.
method Causal machine learning algorithms, subgroup analysis, optimal policy learning.
result Only two coupon categories (drugstore and other food) have a significant positive impact on sales.
Develops algorithm to reduce real-world inequality.
problem Reduces inequality in real-world disparities.
method Impact remediation framework using social science insights and constrained optimization.
result Optimal intervention policies discovered to improve equity.
Study shows COVID-19 cases increase stock market volatility in Pakistan.
problem Impact of COVID-19 on stock market volatility in Pakistan.
method Used vector autoregressive (VAR) model to analyze data from February 25, 2020 to December 7, 2020.
result A shock to total daily coronavirus cases in Pakistan leads to a significant increase in stock market volatility.
Emerging economies use countercyclical policies to manage crises and dominant currencies.
problem Managing economic crises and fluctuations in dominant currencies like USD and EUR.
method Theoretical analysis, case studies, econometric modeling.
result Emerging economies can stabilize growth with countercyclical monetary policies.
CEILS generates feasible counterfactual explanations by considering causal impacts.
problem Current counterfactual explanations lack feasibility and causal impact consideration.
method CEILS integrates causal reasoning into existing counterfactuals generation algorithms.
result CEILS provides feasible recommendations to achieve desired outcomes.
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.
New estimator reduces bias-variance tradeoff in Markovian interference experiments.
problem Estimating impact of interventions in systems with limited resources.
method Differences-In-Q (DQ) estimator for on-policy policy evaluation.
result DQ estimator has exponentially smaller variance than off-policy methods.
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…
Estimates treatment effects in rare extreme events using EVT.
problem Estimating treatment effects in rare, impactful events like extreme climate events.
method Introduces a novel framework using EVT and multivariate regular variation for consistent treatment effect estimation.
result Developed a consistent estimator for extreme treatment effects with rigorous non-asymptotic analysis.
New algorithm optimizes interventions under network interference, scaling to large networks.
problem Optimal policy learning under network interference where one individual's treatment affects others.
method Developed a scalable Thompson sampling algorithm for dynamic networks.
result Proved a Bayesian regret bound that is sublinear in network size and rounds.
Action chunking and data exploration improve behavior cloning in robotics.
problem Exponential errors in learning from demonstrations for continuous control tasks.
method Action chunking and exploratory data collection.
result Control-theoretic stability is key to improving imitation learning.
Updating predictive models introduces bias, leading to miscalibration.
problem Bias introduced when updating predictive models after interventions.
method Proposed a causal framework and partially observed Markov decision process.
result Successive predictive scores may converge to undesirable outcomes.
Synthetic Interventionsecon.EM
Synthetic interventions extend SC method to multiple treatments.
problem Evaluating the impact of multiple treatments in panel data.
method Low-rank tensor factor model for latent factors of treatments.
result Consistent and asymptotically normal estimators for synthetic interventions.
Phenomenological Causalitystat.ME
A new concept of causality for abstract phenomena.
problem Unclear definition of causality in real-life variables.
method Introduces 'phenomenological causality' based on elementary actions.
result Defines causal structure without hard-wired links.
Bayesian Causal Forest models estimate treatment effects with noncompliance.
problem Estimating treatment effects with noncompliance and varying compliance rates.
method Bayesian Causal Forest model for binary response variables.
result Flexibly estimate heterogeneous treatment effects among compliers.
Overlap between treatment groups is required for non-parametric estimation of causal effects. If a subgroup of subjects always receives the same intervention, we cannot estimate the effect of intervention changes on that subgroup without further assumptions. When overlap does not hold globally, characterizing local reg…
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.
Study shows how financial report sentiment impacts bank profitability.
problem Understanding causal effects of financial report sentiment on bank profitability.
method Causal forest machine learning methodology, FinancialBERT sentiment scores, SHAP analysis, comprehensive dataset.
result Statistically significant causal associations between balance sheet and expense management variables and profitability.
Paper uses DDPG to learn optimal execution strategies in dynamic markets.
problem Learning non-Markovian optimal execution strategies in dynamic financial markets.
method Introduces a novel actor-critic algorithm based on DDPG for transient price impact modeling.
result Successfully approximates optimal execution strategy through numerical experiments.
In the current environment of financial distress, many governments are likely to soon become major holders of financial assets, but the policy debate focuses only on the likelihood and extent of short-term market stabilization. This paper shows that government intervention and propping up are likely to lead to long-ter…
Study uses ML and causal analysis to predict student performance factors.
problem Understanding socio-academic and economic factors affecting student performance.
method Employed machine learning techniques and causal analysis on 1,050 student profiles.
result Ridge Regression achieved robust predictions with MAE of 0.12 and MSE of 0.024.
New research on Shapley values for feature attribution in machine learning, considering model vs. data fidelity.
problem Controversy in connecting machine learning models to coalitional games, differing approaches.
method Investigates two approaches: interventional vs. observational conditional expectation Shapley values for linear models.
result The choice between model and data fidelity depends on the specific application.
A method to detect spillover effects and select valid donors for synthetic control models.
problem Identifying valid donors in synthetic control models when spillover effects are possible.
method Theoretical grounding and practical method using pre-intervention data to identify donor values and debias causal estimates.
result A Theorem that identifies assumptions for identifying donor values and debias causal estimates.
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.
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.