Provably Efficient Neural Estimation of Structural Equation Model: An Adversarial Approach

Abstract

Structural equation models (SEMs) are widely used in sciences, ranging from economics to psychology, to uncover causal relationships underlying a complex system under consideration and estimate structural parameters of interest. We study estimation in a class of generalized SEMs where the object of interest is defined as the solution to a linear operator equation. We formulate the linear operator equation as a min-max game, where both players are parameterized by neural networks (NNs), and learn the parameters of these neural networks using the stochastic gradient descent. We consider both 2-layer and multi-layer NNs with ReLU activation functions and prove global convergence in an overparametrized regime, where the number of neurons is diverging. The results are established using techniques from online learning and local linearization of NNs, and improve in several aspects the current state-of-the-art. For the first time we provide a tractable estimation procedure for SEMs based on NNs with provable convergence and without the need for sample splitting.

Publication
arXiv:2007.01290
Luofeng Liao
Luofeng Liao
MS Student

Prior to graduate school, he received B.S. degree in Computer Science at Fudan University in June 2019. His research interests include high-dimensional statistis and distributed optimization.

You-Lin Chen
You-Lin Chen
PhD Student

You-Lin Chen is currently a statistics PhD candidate at the University of Chicago advised by Mladen Kolar. He pursues his research interests in machine learning, stochastic and non-convex optimization, high-dimensional statistics.

Mladen Kolar
Mladen Kolar
Associate Professor of Econometrics and Statistics

Mladen Kolar is an Associate Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. His research is focused on high-dimensional statistical methods, graphical models, varying-coefficient models and data mining, driven by the need to uncover interesting and scientifically meaningful structures from observational data.

Related