arXiv research

MEC-Cox: A Machine-Learning-Assisted Generalized Entropy Calibration Method for Estimating ATT Marginal Hazard-Ratio

New estimator improves ATT estimation efficiency with external controls.

Bayesian methods improve DiD analysis for ATT estimation.

In the absence of unobserved confounders, matching and weighting methods are widely used to estimate causal quantities including the Average Treatment Effect on the Treated (ATT). Unfortunately, these methods do not necessarily achieve their goal of making the multivariate distribution of covariates for the control gro…

Modelling and exploiting teammates' policies in cooperative multi-agent systems have long been an interest and also a big challenge for the reinforcement learning (RL) community. The interest lies in the fact that if the agent knows the teammates' policies, it can adjust its own policy accordingly to arrive at proper c…

Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, and Newey (2016) provide a generic double/de-biased machine learning (DML) approach for obtaining valid inferential statements about focal parameters, using Neyman-orthogonal scores and cross-fitting, in settings where nuisance parameters are estimated using a new gene…

Paper extends causal inference methods beyond unconfoundedness and overlap assumptions.

New algorithms speed up attention computation for large models by limiting matrix entries.

Paper proves optimality of doubly robust estimators for treatment effects.

Novel approach integrates Multivariate Square-root Lasso into Synthetic Control for high-dimensional data.

GACAN combines multi-granularity time series for traffic forecasting.

Novel framework combines tree-based discretization and ILP matching for causal inference.

A new method estimates treatment effects without strong assumptions.

This paper investigates robust and efficient DR/RDR estimators for WATEs.

Estimating treatment effects in time series with hidden confounding.

Python package automates causal parameter estimation using Riesz regression.

S-DIDML integrates structural DID with ML for causal inference in high-dimensional data.

The paper argues for using Neyman orthogonal score for balancing in debiased machine learning.

We consider inference about a scalar parameter under a non-parametric model based on a one-step estimator computed as a plug in estimator plus the empirical mean of an estimator of the parameter's influence function. We focus on a class of parameters that have influence function which depends on two infinite dimensiona…

Study examines market reactions and spillovers in Japanese bank mergers using multiple methods.

New estimators outperform maximum likelihood without hyper-parameter estimation.

New estimator reduces kernel mean estimation error.

Dual Bayesian Affine Estimators for Wiener-type state-space models

Enhances gradient estimates for Hermitian Monge-Ampère equations.

Paper proposes robust estimators for GANs under Wasserstein contamination.

New framework converts offline to online estimation using black-box offline estimators.

Proposes variational autoencoder for efficient MMSE estimation.

Paper improves Fisher information estimation methods.

Proposes a robust estimator for RD designs.

New estimator reduces variance in discrete random variables.

SCOPE estimator improves covariance and precision matrix estimation.

We present a multi-task learning approach to jointly estimate the means of multiple independent data sets. The proposed multi-task averaging (MTA) algorithm results in a convex combination of the single-task maximum likelihood estimates. We derive the optimal minimum risk estimator and the minimax estimator, and show t…

Obtaining more accurate equity value estimates is the starting point for stock selection, value-based indexing in a noisy market, and beating benchmark indices through tactical style rotation. Unfortunately, discounted cash flow, method of comparables, and fundamental analysis typically yield discrepant valuation estim…

The maximum mean discrepancy (MMD) is a kernel-based distance between probability distributions useful in many applications (Gretton et al. 2012), bearing a simple estimator with pleasing computational and statistical properties. Being able to efficiently estimate the variance of this estimator is very helpful to vario…

Stochastic volatility modelling of financial processes has become increasingly popular. The proposed models usually contain a stationary volatility process. We will motivate and review several nonparametric methods for estimation of the density of the volatility process. Both models based on discretely sampled continuo…

A new copula estimation method using classification.

This paper reviews SDR methods for multivariate response regression.

Density ratio estimation is a vital tool in both machine learning and statistical community. However, due to the unbounded nature of density ratio, the estimation procedure can be vulnerable to corrupted data points, which often pushes the estimated ratio toward infinity. In this paper, we present a robust estimator wh…

TAKDE optimizes kernel density estimation for real-time dynamic processes.

We introduce two new estimators of the bivariate Hurst exponent in the power-law cross-correlations setting -- the cross-periodogram and local $X$-Whittle estimators -- as generalizations of their univariate counterparts. As the spectrum-based estimators are dependent on a part of the spectrum taken into consideration …

Paper bridges score estimation to parameter and density estimation in DDPMs.

New method for fast volatility estimation robust to change points.

ROME improves density estimation for multi-modal, non-normal data.

Paper introduces VDE, a variance-reduced determinant estimator.

New estimator improves mutual information estimation.

Private estimation of many quantiles using differential privacy.

Paper proposes robust LAD estimators for 2D sinusoidal model, proving consistency and normality.

Combines multiple OPE estimators into a more accurate and efficient estimate.