New method estimates individual dose-response curves for any number of treatments.
problem Estimating individual dose-response curves for varied exposures.
method Neural network approach for learning counterfactual representations.
result Set a new state-of-the-art in estimating individual dose-response curves.
ContiVAE estimates individual dose-response curves from unobserved confounders using observational data.
problem Estimating causal effects of continuous treatments considering unobserved confounders.
method Variational auto-encoder with a Tilted Gaussian prior distribution modeling hidden confounders as latent variables.
result ContiVAE outperforms existing methods by up to 62% in predicting individual dose-response curves.
Proposes a method for generating prediction intervals in dose-response models using conformal prediction.
problem Uncertainty quantification in continuous treatments for personalized healthcare decisions.
method Causal dose-response problem framed as covariate shift, using weighted conformal prediction with propensity estimation and kernel functions.
result Demonstrates the significance of covariate shift assumptions for robust prediction intervals.
Bayesian model for cancer drug studies maps dose-response curves.
problem Mapping dose-response curves in cancer drug studies.
method Bayesian Tensor Filtering (BTF) with low-dimensional embeddings and structured shrinkage priors.
result BTF outperforms state-of-the-art methods in cancer drug studies.
Paper develops methods to estimate derivative of dose-response curve for continuous treatments.
problem Estimating the derivative of the dose-response curve for continuous treatments.
method Doubly robust (DR) inference method using kernel smoothing, bias-corrected IPW and DR estimators.
result Proposes novel bias-corrected IPW and DR estimators for continuous treatments.
Controlled interventions provide the most direct source of information for learning causal effects. In particular, a dose-response curve can be learned by varying the treatment level and observing the corresponding outcomes. However, interventions can be expensive and time-consuming. Observational data, where the treat…
Proposes estimators for complex dose-response curves using kernel methods.
problem Estimating complex dose-response curves with continuous treatments, mediators, and covariates.
method Kernel ridge regression with sequential kernel embedding technique.
result Simple estimators for mediated and time-varying dose response curves with nonasymptotic uniform rates.
Proposes a method to estimate causal effects of continuous treatments using instrumental variables.
problem Estimating causal effects of continuous treatments in the presence of unmeasured confounders.
method Introduces a novel framework using instrumental variables and a uniform regular weighting function to identify and estimate average dose-response functions.
result Establishes the asymptotic properties of the proposed methods for estimating average dose-response functions.
Proposes DeepSDRF for continuous treatment recommendation from clinical survival data.
problem Continuous treatment recommendation in medical settings with survival data.
method Deep Survival Dose Response Function (DeepSDRF) for learning conditional average dose response (CADR) function.
result Similar performance of recommender algorithms based on random search and reinforcement learning.
Method bounds continuous-valued treatment effects when confounding variables are hidden.
problem Inferring causal effects of continuous treatments when hidden confounders are present.
method Novel methodology to bound average and conditional average continuous-valued treatment effects.
result Method gives tighter coverage of true dose-response curve than existing methods.
Kernel methods identify treatment effects with unobserved confounding using negative controls.
problem Learning causal relationships with unmeasured confounding.
method Kernel ridge regression algorithms for nonparametric treatment effects.
result Uniform consistency and finite sample rates of convergence proved.
Kernel method estimates long-term effects from short-term data.
problem Estimating long-term effects from short-term data in continuous actions.
method Kernel ridge regression to embed and extrapolate long-term effects.
result Uniform consistency and nonasymptotic error bounds for the estimator.
Paper introduces new estimator for continuous treatment effects.
problem Estimating the average dose-response function of continuous treatments.
method Utilizes ADML and DML tools, with a novel debiasing method.
result Proves asymptotic normality and shows good performance in simulations.
SHIFT improves robustness in estimating dose-response functions with heavy-tailed contamination.
problem Outliers bias estimates of average dose-response functions in heavy-tailed data.
method SHIFT combines cross-fit nuisance orthogonalization, Welsch-loss, and defensive OLS refit.
result SHIFT reduces RMSE from 1.03 to 0.33 on localized contamination test.
Many modern data sets are sampled with error from complex high-dimensional surfaces. Methods such as tensor product splines or Gaussian processes are effective/well suited for characterizing a surface in two or three dimensions but may suffer from difficulties when representing higher dimensional surfaces. Motivated by…
Develops a scalable model for drug combination prediction in cancer.
problem Accurate prediction of drug combinations for cancer treatment.
method Permutation invariant multi-output Gaussian Processes with variational approximation and deep generative model.
result Model efficiently borrows information across drug combinations and provides uncertainty quantification.
Develops scalable methods to assess sensitivity and uncertainty in continuous treatment effects.
problem Estimating effects of continuous-valued interventions from observational data, especially when ignorability and positivity assumptions are violated.
method Continuous treatment-effect marginal sensitivity model (CMSM), scalable algorithm, uncertainty-aware deep models.
result Derives bounds that agree with observed data and a defined level of hidden confounding.
The literature of heavy tails (typically) starts with a random walk and finds mechanisms that lead to fat tails under aggregation. We follow the inverse route and show how starting with fat tails we get to thin-tails when deriving the probability distribution of the response to a random variable. We introduce a general…
Proposes a new method for estimating non-pathwise differentiable functional parameters.
problem Estimating dose-response curves for continuous exposure.
method Targeted Highly Adaptive Lasso (HAL) for non-pathwise differentiable functional parameters.
result The Targeted HAL-MLE achieves dimension-free rates up to log(n) factors and outperforms other methods in simulations.
New framework for managing medical risks using convex responses.
problem Medical risk management and dosing optimization.
method Analyzes convex and concave dose-response functions, defines antifragility.
result Proposes a mathematical framework for integrating nonlinearities in oncology.
Proposes VCNet for estimating ADRFs of continuous treatments.
problem Estimating ADRFs of continuous treatments from observational data.
method VCNet for improved model expressiveness and continuity; targeted regularization for finite sample performance.
result Improves model expressiveness and continuity of ADRFs.
Kernel ridge regression for causal inference with missing data.
problem Estimating treatment effects with missing data in selected samples.
method Kernel ridge regression estimators for nonparametric dose response curves and semiparametric treatment effects.
result Uniform consistency and finite sample rates for continuous treatment, root-n consistency for discrete treatment.
Proposes a new method to measure and avoid harm in machine learning decisions.
problem Measuring and avoiding harm in machine learning algorithms.
method Formal definition of harm and benefit using causal models, counterfactual objective functions.
result Demonstrates that standard machine learning methods can lead to harmful policies under distributional shifts.
A new algorithm uses concavity in Gaussian processes to optimize decisions in bandit problems.
problem Optimizing decisions in sequential problems with context-dependent rewards.
method Proposes a UCB algorithm using a shape-constrained reward function estimator based on a Gaussian Process model with concavity constraints.
result Derives regret bounds for the proposed UCB algorithm.
The vast majority of current machine learning algorithms are designed to predict single responses or a vector of responses, yet many types of response are more naturally organized as matrices or higher-order tensor objects where characteristics are shared across modes. We present a new machine learning algorithm BaTFLE…
Efficiently estimates mixed effects models with nonlinear components and constraints.
problem Estimating mixed effects models with nonlinear components and constraints.
method Developed an efficient approach for mixed effects models with trimming in the marginal likelihood.
result More accurate and computationally efficient estimates in the presence of outliers.
GCF estimates heterogeneous treatment effects for continuous treatments in online marketplaces.
problem Estimating heterogeneous treatment effects for continuous treatments in online marketplaces.
method Kernel-based doubly robust estimator and distance-based splitting criterion.
result GCF estimates heterogeneous treatment effects for continuous treatments effectively.
Efficient estimators for smooth Hilbert-valued parameters with theoretical guarantees.
problem Estimating smooth Hilbert-valued parameters with theoretical guarantees.
method Pathwise differentiable Hilbert-valued parameters, efficient influence functions, regularized one-step estimators.
result Theoretical guarantees for efficient estimators even when nuisance functions are arbitrary.
New fairness concept PIIF balances individual and preference-based fairness.
problem Fairness in decision-making systems when individuals have diverse preferences.
method Introduces preference-informed individual fairness (PIIF) as a relaxation of IF and EF.
result PIIF allows for more favorable outcomes than IF while providing more flexibility than EF.
New method certifies individual fairness in representations.
problem Ensuring fairness in data representations without sacrificing utility.
method Mapping similar individuals to close latent representations to certify individual fairness.
result Certifies individual fairness for existing and new data points.
New method approximates Individual Fairness using human judgments.
problem Enforcing fairness in classification tasks.
method Approximates a metric for Individual Fairness based on human queries.
result Constructs hypotheses for metric approximations that generalize.
The study analyzes the conflict between group fairness and individual fairness in machine learning.
problem The conflict between group fairness (optimal statistical parity) and individual fairness in machine learning.
method Established sufficient conditions for the compatibility between optimal statistical parity and individual fairness requirements.
result Identified regions along the Pareto frontier that satisfy individual fairness requirements.
Proposes individual fairness for clustering, making data points prefer their own cluster.
problem No fair clustering for clustering data points.
method Introduces a new fairness notion for clustering and studies its feasibility and heuristics.
result Individual fairness for clustering is NP-hard in general but feasible for one-dimensional data.
Algorithm learns similarity metrics for individual fairness.
problem Difficulty in learning similarity metrics for individual fairness.
method Gradient descent and Bradley-Terry model for pairwise comparisons.
result Algorithm converges to ground truth metric for individual fairness.
Proposes a method to learn fair classifiers without restrictive assumptions.
problem Fairness in machine learning decisions for individuals.
method Defines PIU and optimizes to control its upper bound.
result Guarantees fairness for each individual without restrictive assumptions.
This paper introduces individual fairness in clustering using f-divergence.
problem Ensuring fair clustering by treating similar individuals similarly.
method Uses f-divergence to measure statistical similarity and assigns individuals to probability distributions over cluster centers. result Provides an algorithm with provable approximation guarantee for clustering with individual fairness constraints.
Proposes new fairness definitions for classification tasks combining statistical and individual fairness.
problem Combining statistical and individual fairness in classification tasks.
method Designs an oracle-efficient algorithm for fair empirical risk minimization.
result The ERM solution generalizes to new individuals and tasks.
Social media reduces individual investors' disposition effect through negative information.
problem The disposition effect in individual investors selling profitable assets too early and holding onto losing assets for too long.
method Analysis of post data and trading data from Xueqiu.com.
result Social media information significantly reduces the disposition effect.
Paper operationalizes individual fairness using side-information and a unified representation.
problem Difficulty in eliciting a human specification of a similarity metric for individual fairness.
method Proposes a Pairwise Fair Representation (PFR) model that learns from fairness graph and side-information.
result Unified PFR model effectively operationalizes individual fairness without human specification.
The paper clarifies the distinction between CATE and ITE under ignorability assumptions.
problem Confusion between CATE and ITE hinders personalized effect estimation.
method Clarifies the distinction between CATE and ITE under ignorability assumptions.
result CATE and ITE are not necessarily the same under ignorability assumptions.
EOI enhances individuality in multi-agent systems.
problem Improving multi-agent cooperation through individuality.
method A probabilistic classifier that predicts agent identity based on observations, with intrinsic rewards and regularizers.
result EOI significantly outperforms existing methods in multi-agent cooperative scenarios.
Deep learning models can infer individual trajectories from sparse data.
problem Learning individual dynamics from limited data points.
method Combining variational autoencoders (VAEs) with ordinary differential equations (ODEs) for dynamic modeling.
result Deep learning can recover individual trajectories from sparse data, but requires careful adaptation.
The definition of preferences assigned to individuals is a concept that concerns many disciplines, from economics, with the search of an acceptable outcome for an ensemble of individuals, to decision making an analysis of vote systems. We are concerned in the phenomena of good selection and economic fairness. In Arrow'…
Post-processing corrects bias in ML systems without retraining.
problem Correcting bias in ML systems that are already in use.
method Proposes general post-processing algorithms for individual fairness based on graph Laplacian regularization.
result Empirically, post-processing algorithms correct individual biases in large-scale NLP models while preserving accuracy.
Social learning can make financial markets inefficient, but individual learning can fix this.
problem Inefficiencies in financial markets due to social learning.
method Study of the Minority Game model with social and individual learning mechanisms.
result Individual learning can rescue a population from the inefficiencies caused by social learning.
Paper resolves apparent conflict between group and individual fairness.
problem Apparent conflict between group and individual fairness in machine learning.
method Theoretical discussions from fair machine learning, political, and legal philosophy.
result Individual and group fairness are not fundamentally in conflict.
Avoids resentment in classifier fairness by using monotonic models.
problem Resentment in demographic fairness criteria.
method Monotonic constrained machine learning models.
result Avoids both individual and group resentment.
The paper tackles individualized decision-making under unmeasured confounding, providing a novel minimax solution and a paradox.
problem Unmeasured confounding in causal inference leads to biased estimates and affects individualized decision-making.
method The authors establish a formal link between individualized decision-making under partial identification and classical decision theory, providing a minimax solution and a paradox.
result A novel minimax solution for individualized decision-making/policy assignment is provided, and an interesting paradox is drawn.