arXiv subjects

Daeyoung Ham

Publications and source records attributed to Daeyoung Ham.

5 recordsLinked to original sources

arXiv2026

Black-Box Knowledge Transfer across Distinct Feature Sets

Pre-trained black-box predictive functions encode knowledge distilled from massive datasets and extensive computation. However, when the available input features differ from those the black box expects, direct use is infeasible. We introduce a method for transferring predictive knowledge from the black box to a new, heterogeneous input space. Our approach decomposes the target regression function into a transferable component, which the black box can inform, and a non-transferable component, which captures information unique to the new space. We propose a two-step neural network procedure, estimating the transferable component from abundant unlabeled feature pairs that bridge the two input spaces and the non-transferable component from limited labels. We derive prediction risk bounds that improve on those of a non-transfer alternative when the non-transferable component is small or smooth, and the procedure adapts to either case. Under additional conditions, the worst-case risk of our estimator is of strictly smaller polynomial order than the minimax risk of estimation from the labeled data alone. We extend the framework to multiple black boxes, each on its own input space, and show that aggregation can reduce prediction error relative to the best single black box. Simulated and real data demonstrate the practical value of the method.

stat.ML↗

arXiv2026

Debiased inference for proximal dose-response function

In this paper, we study nonparametric inference for the causal dose-response curve of a continuous-treatment under unmeasured confounding by leveraging treatment- and outcome-inducing confounding proxies. To estimate the curve, we introduce a novel proximal doubly robust pseudo-outcome whose conditional mean given treatment equals the dose-response curve whenever either bridge function is correctly specified, thereby addressing a key gap in proximal causal inference for continuous-treatments. Furthermore, we derive an influence function for its smoothed causal estimand, and construct a cross-fitted debiased local-linear estimator with a proper local-quadratic bias correction. We establish pointwise and finite-dimensional asymptotic normality and a uniform Gaussian approximation over compact treatment intervals. Both smoothing bandwidths may have the mean-squared-error-optimal order without undersmoothing, while cross-fitting accommodates flexible bridge estimators under a product convergence rate conditions without fitted-class entropy restrictions. We also develop practical bandwidth selectors, pointwise confidence intervals, and simultaneous confidence bands. Extensive simulations and a data analysis highlight the practical performance of the proposed method under latent confounding and multiple proxies.

stat.ME↗

arXiv2025

Sparse Multivariate Linear Regression with Strongly Associated Response Variables

We propose new methods for multivariate linear regression when the regression coefficient matrix is sparse and the error covariance matrix is dense. We assume that the error covariance matrix has equicorrelation across the response variables. Two procedures are proposed: one is based on constant marginal response variance (compound symmetry), and the other is based on general varying marginal response variance. Two approximate procedures are also developed for high dimensions. We propose an approximation to the Gaussian validation likelihood for tuning parameter selection. Extensive numerical experiments illustrate when our procedures outperform relevant competitors as well as their robustness to a moderate degree of model misspecification.

stat.ME↗

arXiv2024

Doubly robust estimation and inference for a log-concave counterfactual density

We consider the problem of causal inference based on observational data (or the related missing data problem) with a binary or discrete treatment variable. In that context, we study inference for the counterfactual density functions and contrasts thereof, which can provide more nuanced information than counterfactual means and the average treatment effect. We impose the shape-constraint of log-concavity, a type of unimodality constraint, on the counterfactual densities, and then develop doubly robust estimators of the log-concave counterfactual density based on augmented inverse-probability weighted pseudo-outcomes. We provide conditions under which the estimator is consistent in various global metrics. We also develop asymptotically valid pointwise confidence intervals for the counterfactual density functions and differences and ratios thereof, which serve as a building block for more comprehensive analyses of distributional differences. We also present a method for using our estimator to implement density confidence bands.

stat.ME↗

arXiv2024

Fitted value shrinkage

We propose a penalized least-squares method to fit the linear regression model with fitted values that are invariant to invertible linear transformations of the design matrix. This invariance is important, for example, when practitioners have categorical predictors and interactions. Our method has the same computational cost as ridge-penalized least squares, which lacks this invariance. We derive the expected squared distance between the vector of population fitted values and its shrinkage estimator as well as the tuning parameter value that minimizes this expectation. In addition to using cross validation, we construct two estimators of this optimal tuning parameter value and study their asymptotic properties. Our numerical experiments and data examples show that our method performs similarly to ridge-penalized least-squares.

stat.ME↗