XTNet estimates complex cross-treatment effects in multi-category, multi-valued settings.
problem Challenges in estimating causal effects for multi-category, multi-valued treatments.
method Dynamic Neural Masking for capturing treatment interactions without restrictive assumptions.
result XTNet consistently outperforms state-of-the-art baselines in multi-category, multi-valued treatment effect estimation.
This paper analyzes meta-learners for estimating multi-valued treatment effects.
problem Estimating Conditional Average Treatment Effects (CATE) with multi-valued treatments.
method The paper considers different meta-learners and analyzes their error bounds.
result Meta-learners perform well as the number of treatments increases, improving upon naive extensions.
New method estimates causal effects with multi-valued, time-varying treatments.
problem Estimating causal effects with complex time-varying exposures.
method Combines machine learning and semiparametric efficiency theory.
result Proposes an efficient, asymptotically normal estimator for marginal structural models.
The paper constructs a multi-valued inverse of quasiregular maps and develops pull-back theory for differential forms.
problem Understanding multi-valued inverses of quasiregular maps and their properties.
method Using Almgren's framework of multi-valued maps and developing pull-back theory for differential forms.
result The multi-valued inverse is a quasiregular ω-curve with respect to a natural n-form ω. The paper uses neural networks to estimate treatment effects even with many confounders.
problem Estimating treatment effects with a growing number of confounders.
method General optimization framework using neural networks to approximate nuisance functions.
result Neural networks can handle a diverging number of confounders and alleviate the curse of dimensionality.
We construct several types of multi-valued solutions to the Monge-Ampere equation in higher dimensions.
Extends Campanato theory to multi-valued functions for geometric variational problems.
problem Regularity of multi-valued functions in geometric variational problems.
method Adapting Campanato's ideas to multi-valued functions, proving regularity theorems.
result Established regularity for multi-valued harmonic functions and stationary integral varifolds.
We apply the technique of integrable extensions to the symmetry pseudo-group of the dKP-hyper CR interpolating equation. This allows us to find a covering for this equation and to construct multi-valued Einstein-Weyl structures.
In this paper we prove that an embedded and simply connected constant mean curvature surface with curvature large at a point contains a multi-valued graph around that point on the scale of ∣A∣2, where ∣A∣2 is the norm squared of the second fundamental form. This generalizes Colding and Minicozzi's result for mini…
New category theory for complex projective plane sections.
problem Defining multi-valued Morse homotopy for complex projective plane.
method Introducing multi-valued Morse homotopy category and showing equivalence to DG category of holomorphic vector bundles.
result Multi-valued Morse homotopy category is equivalent to DG category of holomorphic vector bundles.
In the early 1980's Almgren developed a theory of Dirichlet energy minimizing multi-valued functions, proving that the Hausdorff dimension of the singular set (including branch points) of such a function is at most (n−2), where n is the dimension of its domain. Almgren used this result in an essential way to show t…
VOWEL trains WTA-SNNs for multi-valued events, overcoming resource limitations.
problem Training WTA-SNNs for multi-valued events is challenging due to non-differentiability and recurrent behavior.
method Develops a variational online local training rule (VOWEL) for WTA-SNNs using local pre- and post-synaptic information and a common reward signal.
result VOWEL outperforms conventional binary SNNs in real-world neuromorphic datasets with multi-valued events.
This paper is the second in a series where we attempt to give a complete description of the space of all embedded minimal surfaces of fixed genus in a fixed (but arbitrary) closed 3-manifold. The key for understanding such surfaces is to understand the local structure in a ball and in particular the structure of an emb…
The Chekanov theorem generalizes the classic Lyusternik-Shnirel'man and Morse theorems concerning critical points of a smooth function on a closed manifold. A Legendrian submanifold Λof space of 1-jets of the functions on a manifold M defines a multi-valued function whose graph is the projection of Λin J^0 M = M x R. T…
After appropriate normalizations an embedded disk whose second fundamental form has large norm contains a multi-valued graph, provided the L^P norm of the mean curvature is sufficiently small. This generalizes to non-minimal surfaces a well known result of Colding and Minicozzi.
For multi-valued functions---such as when the conditional distribution on targets given the inputs is multi-modal---standard regression approaches are not always desirable because they provide the conditional mean. Modal regression algorithms address this issue by instead finding the conditional mode(s). Most, however,…
DiffOPF solves multi-valued OPF problems by sampling from system history.
problem Multi-valued and non-convex OPF problems due to system parameter variability.
method DiffOPF treats OPF as a conditional sampling problem, learning from historical data.
result DiffOPF enables statistically credible warm starts with favorable cost and constraint satisfaction trade-offs.
In the 1980's, Almgren developed a theory of multi-valued Dirichlet energy minimizing functions on n dimensional domains and used it, in an essential way, to bound the Hausdorff dimension of the singular sets of area minimizing rectifiable currents of dimension n and codimension ≥2. Recent work of the second …
Paper tackles optimal policy learning with observational data in multi-action scenarios.
problem Optimal policy learning in multi-action settings with observational data.
method Review of estimation approaches, analysis of risk preference, discussion of potential failures.
result Average regret of a policy with multi-valued treatment is contingent on the decision-maker's attitude towards risk.
Study on a weighted Suita conjecture for higher derivatives and their geometric properties.
problem Analyzing the Suita conjecture for higher derivatives with weights.
method Examining the set of points for equality in a weighted Suita conjecture and relating it to harmonic functions and Dirichlet problems.
result Relations between the set of points and integer-valued points of harmonic functions and Dirichlet problems for planar domains.
In this paper, we consider multi-valued graphs with a prescribed real analytic interface that minimize the Dirichlet energy. Such objects arise as a linearized model of area minimizing currents with real analytic boundaries and our main result is that their singular set is discrete in 2 dimensions. This confirms (and p…
We analyze a notion of multiple valued sections of a vector bundle over an abstract smooth Riemannian manifold, which was suggested by W. Allard in the unpublished note "Some useful techniques for dealing with multiple valued functions" and generalizes Almgren's Q-valued functions. We study some relevant properties o…
We prove an Alexandrov type theorem for a quotient space of H2×R. More precisely we classify the compact embedded surfaces with constant mean curvature in the quotient of H2×R by a subgroup of isometries generated by a parabolic translation along horocycles of $\mathbb …
This is the second paper of a series of three on the regularity of higher codimension area minimizing integral currents. Here we perform the second main step in the analysis of the singularities, namely the construction of a center manifold, i.e. an approximate average of the sheets of an almost flat area minimizing cu…
Alternative proof and extension of curvature estimates for minimal immersions.
problem Curvature estimates and Bernstein-type theorems for minimal immersions.
method Iteration method à la De Giorgi, ε-regularity theorem, Caccioppoli inequalities.
result Extension of Schoen--Simon--Yau and Schoen--Simon theorems to 6-dimensional stable minimal immersions.
In this paper, we propose an active learning method for an inverse problem that aims to find an input that achieves a desired structured-output. The proposed method provides new acquisition functions for minimizing the error between the desired structured-output and the prediction of a Gaussian process model, by effect…
In this article, we study local holomorphic isometric embeddings from ${\BB}^n$ into ${\BB}^{N_1}\times... \times{\BB}^{N_m}$ with respect to the normalized Bergman metrics up to conformal factors. Assume that each conformal factor is smooth Nash algebraic. Then each component of the map is a multi-valued holomorphic m…
GraphITE estimates individual effects of graph-structured treatments.
problem Estimating individual effects of complex treatment structures.
method Graph neural networks and Hilbert-Schmidt Independence Criterion regularization.
result GraphITE outperforms baselines in estimating treatment effects for large numbers of treatments.
M3E2 neural network estimates multiple treatment effects.
problem Estimating effects of multiple treatments simultaneously.
method Multi-task learning neural network model for multiple treatments, continuous and binary.
result M3E2 outperforms baselines in synthetic datasets.
Proposes a new method to estimate continuous treatment policies and match treatments effectively.
problem Current methods struggle with continuous treatment policies and complex matching.
method Formulates treatment effectiveness as a parametrizable model, using deep learning for optimization.
result Significant improvement in treatment effectiveness and matching efficiency.
A clustering may be considered as fair on pre-specified sensitive attributes if the proportions of sensitive attribute groups in each cluster reflect that in the dataset. In this paper, we consider the task of fair clustering for scenarios involving multiple multi-valued or numeric sensitive attributes. We propose a fa…
Framework generates personalized insulin treatment strategies using deep models.
problem Developing optimal personalized treatment strategies for diabetes patients.
method Combines deep generative time series models with decision theory.
result Demonstrated improved personalized insulin treatment strategies for diabetes patients.
Develops deep jump learning for continuous treatment OPE.
problem Estimating mean outcomes under new treatment rules using historical data from different rules.
method Adaptive deep discretization of continuous treatment space using deep learning and multi-scale change point detection.
result Validated method through theoretical results, simulations, and real application to Warfarin Dosing.
Method controls treatment risk in learning beneficial allocations.
problem Learning beneficial treatment allocations with risk control in precision medicine.
method Proposes a certifiable learning method that controls treatment risk with finite samples in the partially identified setting.
result Illustrates method using both simulated and real data.
Optimal adaptive experiment for choosing best treatment with binary outcomes.
problem Choosing the best treatment from binary options in an adaptive experiment.
method Adaptive experiment with two phases: treatment allocation and choice. Neyman allocation method used.
result Neyman allocation is minimax and Bayes optimal, matching lower bounds for regret.
Proposes a fusion method for many treatment groups in ITRs.
problem Challenges in handling many treatment groups with data sparsity and covariate imbalance.
method Calibration-weighted treatment fusion procedure that balances covariates and fuses similar treatments.
result Ensures robust treatment group recovery and policy value compared to existing methods.
Dynamic treatment effects estimated over time using covariate balancing.
problem Estimating treatment effects in panel data with dynamic treatments.
method Dynamic covariate balancing with potential local projections.
result Established inferential guarantees for the proposed method.
Heteroskedasticity biases uplift model rankings, leading to inefficient treatment allocation.
problem Bias in uplift model rankings due to heteroskedasticity.
method Theoretical analysis and simulation on real-world data.
result Heteroskedasticity can cause individuals with high treatment effects to be ranked at the bottom, leading to inefficient treatment allocation.
Estimates heterogeneous treatment effects in panel data with a new method.
problem Estimating heterogeneous treatment effects in panel data with general treatment patterns.
method Partition observations into clusters with similar treatment effects using a regression tree, then estimate average treatment effects for each cluster.
result Our method achieves superior accuracy compared to alternative approaches.
In treatment allocation problems the individuals to be treated often arrive sequentially. We study a problem in which the policy maker is not only interested in the expected cumulative welfare but is also concerned about the uncertainty/risk of the treatment outcomes. At the outset, the total number of treatment assign…
NICE model estimates causal effects for image treatments.
problem Challenges in causal effect estimation for multi-dimensional treatments.
method Proposes NICE model for image treatments, incorporating rich multidimensional information.
result NICE significantly outperforms existing models in estimating causal effects for image treatments.
Personalized medicine aims at identifying best treatments for a patient with given characteristics. It has been shown in the literature that these methods can lead to great improvements in medicine compared to traditional methods prescribing the same treatment to all patients. Subgroup identification is a branch of per…
The paper proposes a method to precisely decompose confounders and estimate treatment effects.
problem Estimating treatment effects from observational data with confounder identification and balancing.
method Learning decomposed representations to identify and balance confounders and non-confounders.
result The method achieves more precise treatment effect estimation than existing methods.
RATE metrics evaluate treatment prioritization rules, subsuming existing methods.
problem Comparing and testing the quality of treatment prioritization rules.
method Rank-weighted average treatment effect (RATE) metrics.
result RATE metrics enable asymptotically exact inference in various study settings.
Selective imputation improves treatment effect estimation from missing data.
problem Missing data complicates treatment effect estimation, especially with treatment variables.
method Introduced mixed confounded missingness (MCM) and selective imputation.
result Selective imputation provides unbiased treatment effect estimates.
TV-SurvCaus improves causal inference for dynamic treatments in survival analysis.
problem Estimating causal effects of time-varying treatments on survival outcomes.
method Representation balancing techniques extended to time-varying treatment regimes with survival outcomes.
result TV-SurvCaus outperforms existing methods in estimating individualized treatment effects with time-varying covariates and treatments.
Develops methods for near-optimal personalized treatment recommendations.
problem Assigning optimal treatments to patients based on individual characteristics.
method Outcome weighted learning framework to estimate near-optimal alternative individualized treatment recommendations (A-ITR).
result Consistency of proposed methods and upper bound for risk between optimal and estimated recommendations.
Given two possible treatments, there may exist subgroups who benefit greater from one treatment than the other. This problem is relevant to the field of marketing, where treatments may correspond to different ways of selling a product. It is similarly relevant to the field of public policy, where treatments may corresp…