Causal Discovery: Latent Variable Models

Description

This lecture by the instructor covers the topic of causal discovery based on latent variable models. It delves into the fundamental problem of causal analysis, distinguishing between assumption-free and assumption-based methods. The lecture explores the challenges of identifying causal relationships in non-Gaussian data and the importance of considering temporal versus instantaneous effects. It introduces the concept of Linear non-Gaussian Acyclic Model (LiNGAM) and explains how non-Gaussianity can break the symmetry between variables, aiding in causal inference. The lecture also discusses the estimation of LiNGAM using Independent Component Analysis (ICA) and the difficulties and solutions in nonlinear Structural Equation Models (SEM) through methods like NonSENS. It concludes by emphasizing the significance of finding the direction of effect in causal discovery and the future prospects in this field.

Official source

https://mediaspace.epfl.ch/media/0_h8j9sz8w

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Related lectures (38)

Related concepts (38)

Structural equation modeling

Structural equation modeling (SEM) is a diverse set of methods used by scientists doing both observational and experimental research. SEM is used mostly in the social and behavioral sciences but it is also used in epidemiology, business, and other fields. A definition of SEM is difficult without reference to technical language, but a good starting place is the name itself. SEM involves a model representing how various aspects of some phenomenon are thought to causally connect to one another.

Causal inference

Causal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system. The main difference between causal inference and inference of association is that causal inference analyzes the response of an effect variable when a cause of the effect variable is changed. The science of why things occur is called etiology, and can be described using the language of scientific causal notation. Causal inference is said to provide the evidence of causality theorized by causal reasoning.

Latent and observable variables

In statistics, latent variables (from Latin: present participle of lateo, “lie hidden”) are variables that can only be inferred indirectly through a mathematical model from other observable variables that can be directly observed or measured. Such latent variable models are used in many disciplines, including political science, demography, engineering, medicine, ecology, physics, machine learning/artificial intelligence, bioinformatics, chemometrics, natural language processing, management, psychology and the social sciences.

Independent component analysis

In signal processing, independent component analysis (ICA) is a computational method for separating a multivariate signal into additive subcomponents. This is done by assuming that at most one subcomponent is Gaussian and that the subcomponents are statistically independent from each other. ICA is a special case of blind source separation. A common example application is the "cocktail party problem" of listening in on one person's speech in a noisy room.

Causality

Causality (also called causation, or cause and effect) is influence by which one event, process, state, or object (a cause) contributes to the production of another event, process, state, or object (an effect) where the cause is partly responsible for the effect, and the effect is partly dependent on the cause. In general, a process has many causes, which are also said to be causal factors for it, and all lie in its past. An effect can in turn be a cause of, or causal factor for, many other effects, which all lie in its future.