16 research outputs found
Identifying Nonlinear 1-Step Causal Influences in Presence of Latent Variables
We propose an approach for learning the causal structure in stochastic
dynamical systems with a -step functional dependency in the presence of
latent variables. We propose an information-theoretic approach that allows us
to recover the causal relations among the observed variables as long as the
latent variables evolve without exogenous noise. We further propose an
efficient learning method based on linear regression for the special sub-case
when the dynamics are restricted to be linear. We validate the performance of
our approach via numerical simulations
Causal Transfer for Imitation Learning and Decision Making under Sensor-shift
Learning from demonstrations (LfD) is an efficient paradigm to train AI
agents. But major issues arise when there are differences between (a) the
demonstrator's own sensory input, (b) our sensors that observe the demonstrator
and (c) the sensory input of the agent we train. In this paper, we propose a
causal model-based framework for transfer learning under such "sensor-shifts",
for two common LfD tasks: (1) inferring the effect of the demonstrator's
actions and (2) imitation learning. First we rigorously analyze, on the
population-level, to what extent the relevant underlying mechanisms (the action
effects and the demonstrator policy) can be identified and transferred from the
available observations together with prior knowledge of sensor characteristics.
And we device an algorithm to infer these mechanisms. Then we introduce several
proxy methods which are easier to calculate, estimate from finite data and
interpret than the exact solutions, alongside theoretical bounds on their
closeness to the exact ones. We validate our two main methods on simulated and
semi-real world data.Comment: It appears in AAAI-202
Econometric modeling of systemic risk: going beyond pairwise comparison and allowing for nonlinearity
Financial instability and its destructive effects on the economy can lead to financial crises due to its contagion or spillover effects to other parts of the economy. Having an accurate measure of systemic risk gives central banks and policy makers the ability to take proper policies in order to stabilize financial markets. Much work is currently being undertaken on the feasibility of identifying and measuring systemic risk. In principle, there are two main schemes to measure interlinkages between financial institutions. One might wish to construct a mathematical model of financial market participant relations as a network/graph by using a combination of information extracted from financial statements like the market value of liabilities of counterparties, or an econometric model to estimate those relations based on financial series. In this paper, we develop a data-driven econometric framework that promotes an understanding of the relationship between financial institutions using a nonlinearly modified Granger-causality network. Unlike existing literature, it is not focused on a linear pairwise estimation. The method allows for nonlinearity and has predictive power over future economic activity through a time-varying network of relationships. Moreover, it can quantify the interlinkages between financial institutions. We also show how the model improve the measurement of systemic risk and explain the link between Granger-causality network and generalized variance decompositions network. We apply the method to the monthly returns of U.S. financial Institutions including banks, broker and insurance companies to identify the level of systemic risk in the financial sector and the contribution of each financial institution
Experimental Design for Causal Effect Identification
Pearl's do calculus is a complete axiomatic approach to learn the
identifiable causal effects from observational data. When such an effect is not
identifiable, it is necessary to perform a collection of often costly
interventions in the system to learn the causal effect. In this work, we
consider the problem of designing the collection of interventions with the
minimum cost to identify the desired effect. First, we prove that this problem
is NP-hard, and subsequently propose an algorithm that can either find the
optimal solution or a logarithmic-factor approximation of it. This is done by
establishing a connection between our problem and the minimum hitting set
problem. Additionally, we propose several polynomial-time heuristic algorithms
to tackle the computational complexity of the problem. Although these
algorithms could potentially stumble on sub-optimal solutions, our simulations
show that they achieve small regrets on random graphs.Comment: 53 pages, 13 figures, extending the findings of our ICML2022 pape
Sharp Analysis of Stochastic Optimization under Global Kurdyka-{\L}ojasiewicz Inequality
We study the complexity of finding the global solution to stochastic
nonconvex optimization when the objective function satisfies global
Kurdyka-Lojasiewicz (KL) inequality and the queries from stochastic gradient
oracles satisfy mild expected smoothness assumption. We first introduce a
general framework to analyze Stochastic Gradient Descent (SGD) and its
associated nonlinear dynamics under the setting. As a byproduct of our
analysis, we obtain a sample complexity of
for SGD when the objective
satisfies the so called -PL condition, where is the degree of
gradient domination. Furthermore, we show that a modified SGD with variance
reduction and restarting (PAGER) achieves an improved sample complexity of
when the objective satisfies the average
smoothness assumption. This leads to the first optimal algorithm for the
important case of which appears in applications such as policy
optimization in reinforcement learning.Comment: The work was submitted for review in May, 2022 and was accepted to
NeurIPS 2022 in Sep, 202