This chapter discusses structural econometric approaches to auctions. Remarkably, much of
what can be learned from auction data can be learned without restrictions beyond those …

Testing conditional independence (CI) for continuous variables is a fundamental but
challenging task in statistics. Many tests for this task are developed and used increasingly …

We propose a coefficient of conditional dependence between two random variables Y and Z
given a set of other variables X 1,…, X p, based on an iid sample. The coefficient has a long …

We propose a general new method, the conditional permutation test, for testing the
conditional independence of variables X and Y given a potentially high dimensional random …

Observational studies in which the effect of a nonrandomized treatment on an outcome of
interest is estimated are common in domains such as labour economics and epidemiology …

We propose and study a class of simple, nonparametric, yet interpretable measures of
conditional dependence, which we call kernel partial correlation (KPC) coefficient, between …

Statistical inference on conditional dependence is essential in many fields including genetic
association studies and graphical models. The classic measures focus on linear conditional …

We develop semiparametric tests for conditional independence in time series models of
causal effects. Our approach is motivated by empirical studies of monetary policy effects and …

Minimax optimal conditional independence testing Page 1 The Annals of Statistics 2021, Vol.
49, No. 4, 2151–2177 https://doi.org/10.1214/20-AOS2030 © Institute of Mathematical …

This article introduces a kernel-based nonparametric inferential procedure to test for
Granger causality in distribution. This test is a multivariate extension of the kernel-based …