The paper proposes a method to explain expert decisions by modeling preferences with 'what if' outcomes.
problem Interpreting and auditing decision-making policies in institutions.
method Integrating counterfactual reasoning into batch inverse reinforcement learning.
result The method effectively recovers accurate and interpretable descriptions of expert behavior.
A new method models continuous-time counterfactual outcomes using neural controlled differential equations.
problem Estimating personalized healthcare outcomes over irregularly sampled data.
method Interpreting data as samples from a continuous-time process, modeling latent trajectory using controlled differential equations, and using adversarial training for time-dependent confounding.
result TE-CDE consistently outperforms existing approaches in irregularly sampled scenarios.
A key challenge in developing and deploying Machine Learning (ML) systems is understanding their performance across a wide range of inputs. To address this challenge, we created the What-If Tool, an open-source application that allows practitioners to probe, visualize, and analyze ML systems, with minimal coding. The W…
Learning representations for counterfactual inference from observational data is of high practical relevance for many domains, such as healthcare, public policy and economics. Counterfactual inference enables one to answer "What if...?" questions, such as "What would be the outcome if we gave this patient treatment $t_…
Proposes a new model to identify unknown counterfactual outcomes for continuous variables.
problem Counterfactual inference for continuous outcomes with strong assumptions.
method Curvature Sensitivity Model to relax assumptions and provide informative bounds.
result Demonstrates effectiveness of the Curvature Sensitivity Model in identifying counterfactual outcomes.
Decision-makers are faced with the challenge of estimating what is likely to happen when they take an action. For instance, if I choose not to treat this patient, are they likely to die? Practitioners commonly use supervised learning algorithms to fit predictive models that help decision-makers reason about likely futu…
Integrating causal machine learning with inherently interpretable models for decision support.
problem Providing causal insights and decision support through machine learning models.
method Proposing an approach that integrates causal machine learning with inherently interpretable models.
result The proposed approach achieves competitive performance in prediction and what-if analysis while offering transparency on the system structure, causal relationships among variables, and functional forms connecting them.
Proposes Causal-Batle for estimating treatment effects in small high-dimensional datasets.
problem Estimating treatment effects with small high-dimensional datasets.
method Adopts transfer learning techniques for causal inference.
result Improves treatment effect estimates in small high-dimensional datasets.
Method explains anomaly detection by generating normal modifications.
problem Complexity of deep learning methods in anomaly detection.
method Generates multiple alternative modifications for anomalies.
result High-quality semantic explanations provided for anomaly detection.
Estimation of individual treatment effect in observational data is complicated due to the challenges of confounding and selection bias. A useful inferential framework to address this is the counterfactual (potential outcomes) model which takes the hypothetical stance of asking what if an individual had received both tr…
Proposes MOC method for better counterfactual explanations in ML models.
problem Difficulties in balancing multiple objectives for counterfactual explanations.
method Translates counterfactual search into a multi-objective optimization problem.
result Returns diverse counterfactuals with different trade-offs and maintains feature diversity.
DeepPPMNet forecasts EMS demand and performs causal analyses for policy-making.
problem Accurate prediction and causal analysis of EMS demand for effective policy-making.
method DeepPPMNet, a LSTM-based framework, globally forecasts and analyzes causal relationships using Granger causality.
result DeepPPMNet outperforms traditional methods in forecasting EMS demand and policy-making.
Variable importance is central to scientific studies, including the social sciences and causal inference, healthcare, and other domains. However, current notions of variable importance are often tied to a specific predictive model. This is problematic: what if there were multiple well-performing predictive models, and …
In quantum computation, series of quantum gates have to be arranged in a predefined sequence that led to a quantum circuit in order to solve a particular problem. What if the sequence of quantum gates is known but both the problem to be solved and the outcome of the so defined quantum circuit remain in the shadow? This…
Proposes a new model for predicting future motion of road actors in autonomous vehicles.
problem Forecasting the long-term future motion of road actors for safe autonomous driving.
method Recurrent graph-based attentional approach with interpretable geometric and social relationships.
result Can produce diverse predictions conditioned on hypothetical or 'what-if' scenarios.
The increasing adoption of machine learning tools has led to calls for accountability via model interpretability. But what does it mean for a machine learning model to be interpretable by humans, and how can this be assessed? We focus on two definitions of interpretability that have been introduced in the machine learn…
Model analyzes mortgage relief during financial hardship.
problem Understanding and optimizing mortgage relief during financial distress.
method Agent-based model of households and servicers.
result Model replicates real-world mortgage studies and provides fine-grained insights.
Survey of counterfactual explanations for time series classification.
problem Generating plausible and actionable counterfactuals for time series data.
method Review of various counterfactual generation methods for time series classification.
result Highlight unique challenges and strengths of existing methods.
Investor optimizes investment timing with future knowledge, overcoming transaction costs.
problem Optimal investment timing with future peeking, constrained by transaction costs.
method Solves control problem with infinite-dimensional memory using Gaussian Volterra integral equations.
result Explicit solution to optimal investment problem in Bachelier setting.
This paper discusses the financial risks faced by the UK Pension Protection Fund (PPF) and what, if anything, it can do about them. It draws lessons from the regulatory regimes under which other financial institutions, such as banks and insurance companies, operate and asks why pension funds are treated differently. It…
PolicySynth improves synthetic data alignment with real data for better campaign decisions.
problem Synthetic data used in decision support systems often leads to incorrect decisions.
method PolicySynth framework that conditions synthetic data on churn scorer to align with real data decisions.
result PolicySynth achieves high strategy simulation fidelity (0.923-0.960) on churn and acquisition datasets.
A new algorithm speeds up matrix operations in Neural Networks.
problem Time-consuming matrix operations in Neural Networks.
method An algorithm that increases the degree of parallelism of matrix multiplication.
result The algorithm speeds up several matrix operations in Neural Networks.
Was it fair that Harry was hired but not Barry? Was it fair that Pam was fired instead of Sam? How can one ensure fairness when an intelligent algorithm takes these decisions instead of a human? How can one ensure that the decisions were taken based on merit and not on protected attributes like race or sex? These are t…
Counterfactuals help compare different scenarios in CI and XAI.
problem Comparing how counterfactuals are used in causal inference and explainable AI.
method Formal definition and comparison of counterfactuals in CI and XAI.
result Identified opportunities for cross-fertilization between CI and XAI.
Cycloids, hipocycloids and epicycloids have an often forgotten common property: they are homothetic to their evolutes. But what if use convex symmetric polygons as unit balls, can we define evolutes and cycloids which are genuinely discrete? Indeed, we can! We define discrete cycloids as eigenvectors of a discrete doub…
An agent explores indefinitely in an environment with unlimited rewards.
problem Balancing exploration and exploitation in environments with unlimited rewards.
method Simple example of an environment with unbounded rewards and optimal agent behavior.
result An optimal agent always explores to maximize rewards, regardless of accumulated knowledge.
New model recognizes emotions with missing modalities, improving accuracy.
problem Handling missing modalities in emotion recognition.
method Transformer-based architecture with cross-attention and self-attention mechanisms.
result Improvement of 37% in predicting arousal values and 30% in valence values compared to baseline.
When can one 3-manifold be transformed to another by a finite sequence of Dehn surgeries which are restricted to preserve the first homology of the manifolds ? What is the resulting equivalence relation on 3-manifolds ? What if the surgery circle is further restricted to lie more deeply in the lower central series of t…
Paper simplifies balancing weights by relaxing outcome assumptions.
problem Estimating missing outcomes in a target population.
method Relaxes outcome assumptions to simplify balancing weights.
result Balancing weights can be simplified with convex loss and minimum worst-case bias.
Text classification on drug SMILES strings yields competitive drug type classification results.
problem Classifying drug types using conventional text classification methods.
method Treated drug SMILES as sentences and applied basic NLP methods for classification.
result Competitive drug type classification results achieved.
A variance swap is a derivative with a path-dependent payoff which allows investors to take positions on the future variability of an asset. In the idealised setting of a continuously monitored variance swap written on an asset with continuous paths it is well known that the variance swap payoff can be replicated exact…
A method corrects bias in estimating a high-dimensional classification rule using auxiliary outcomes.
problem Bias in estimating a high-dimensional classification rule using only one outcome.
method Robust transfer learning approach combining MTL and calibration steps.
result Final estimator achieves lower error than using only the target outcome.
Paper presents a Rashomon Quartet to show different model explanations.
problem Models with similar performance can explain data differently.
method Synthetic dataset with four equally effective models.
result Models with similar performance can explain data differently.
PO-Flow models potential and counterfactual outcomes for personalized treatment decisions.
problem Predicting individualized treatment effects from observational data.
method Continuous normalizing flow (CNF) framework for causal inference.
result Unified approach to potential outcome prediction, treatment effect estimation, and counterfactual prediction.
TOO optimizes stochastic epidemiological models by finding both parameter settings and random seeds.
problem Calibrating stochastic epidemiological models to match empirical observations.
method Gaussian process surrogates and Thompson sampling for optimization.
result Produces actual trajectories consistent with ground truth.
Study finds a method to discover causal relationships that are invariant to marginal distributions.
problem Current causal discovery methods are sensitive to marginal distributions, leading to unreliable results.
method Proposes a non-parametric estimator that marginalizes the marginals to find intrinsic causal relationships.
result The proposed method yields causal estimators competitive with current methodologies and emphasizes uncertainty.
Although deep reinforcement learning agents have produced impressive results in many domains, their decision making is difficult to explain to humans. To address this problem, past work has mainly focused on explaining why an action was chosen in a given state. A different type of explanation that is useful is a counte…
Fuses ITRs for primary and secondary outcomes to minimize harm.
problem Learn an ITR maximizing primary outcome while minimizing harm to secondary outcomes.
method Introduces fusion penalty to encourage similar recommendations for different outcomes. Two algorithms estimate the ITR using surrogate loss functions.
result Agreement rate between primary and secondary optimal ITRs converges faster than ignoring secondary outcomes.
Firms delay write-downs for adverse macroeconomic and industry outcomes but not for firm-specific issues.
problem Timeliness of write-downs for adverse macroeconomic and industry outcomes versus firm-specific issues.
method Comparative analysis of write-downs driven by macroeconomic and industry outcomes versus firm-specific outcomes.
result Firms delay write-downs for adverse macroeconomic and industry outcomes but not for firm-specific issues.
Reduces variance in noisy social outcomes to improve policy evaluation and optimization.
problem Improving access to opportunity through personalized treatment decisions.
method Data-driven dimensionality-reduction using reduced rank regression to denoise multiple outcomes.
result Improves estimation error in policy evaluation and optimization, including on real-world data.
The paper introduces metrics to rank potential outcomes for better decision-making.
problem Optimal action selection in uncertain situations using causal reasoning.
method Introducing two new metrics: probabilities of potential outcome ranking (PoR) and probability of achieving the best potential outcome (PoB). Establishing identification theorems and deriving bounds for these metrics, and presenting estimation methods.
result The estimators' finite-sample properties and their application to a real-world dataset are demonstrated.
Proposes a deep learning framework for estimating counterfactual outcomes.
problem Challenges in estimating individual outcomes under different treatments.
method Deep variational Bayesian framework integrating factual and similar subjects' outcomes.
result Rigorously integrates individual features and similar subjects' responses for counterfactual outcomes.
The study uses transfer learning to compare surgical outcomes across racial/ethnic subgroups.
problem Difficulty in comparing surgical outcomes due to racial/ethnic and geographic differences.
method Causal inference framework and transfer learning to incorporate data from multiple populations.
result Racial and ethnic differences in surgical outcomes are found, with non-Hispanic Black patients experiencing wide variability.
DEBIAS learns causal effects from psychiatric longitudinal data by optimizing outcome weights.
problem Causal inference challenges in psychiatric longitudinal data due to symptom heterogeneity and latent confounding.
method DEBIAS algorithm that optimizes outcome weights to maximize durable treatment effects and minimize confounding.
result DEBIAS consistently outperforms state-of-the-art methods in recovering causal effects for clinically interpretable composite outcomes.
New approach tackles decision-making under predictions that shape outcomes.
problem Challenges in learning optimal decision rules when predictions influence outcomes.
method Introduces performative omniprediction, a predictor that encodes optimal decision rules for multiple objectives.
result Efficient performative omnipredictors exist under a natural restriction of outcome performativity.
Who {\em values} life annuities more? Is it the healthy retiree who expects to live long and might become a centenarian, or is the unhealthy retiree with a short life expectancy more likely to appreciate the pooling of longevity risk? What if the unhealthy retiree is pooled with someone who is much healthier and thus f…
New method identifies proxies for causal effects on multiple outcomes.
problem Estimating causal effects in scenarios with multiple outcomes and treatments.
method Causal discovery method leveraging multiple outcomes as proxies for each treatment effect.
result Parallel studies of multiple outcomes can assist in causal identification.
Study uses surrogate data to improve treatment effect estimation with scarce outcome data.
problem Limited outcome data hinders estimating treatment effects.
method Uses abundant surrogate data to estimate treatment effects without stringent assumptions.
result Improves precision of treatment effect estimation.