EM-based algorithm

Gold definitionUpdated Apr 2, 2026

Definition

An EM-based algorithm iteratively estimates parameters in statistical models, especially those with latent variables or incomplete data. In causal inference, it enables learning parameters of Centralized Gaussian Linear SCMs to estimate identifiable causal effects from observational data.

At a glance

Executive summary

An EM-based algorithm is a method for estimating parameters in statistical models, especially when some information is hidden or incomplete. In the context of causal inference, a new EM-based algorithm helps accurately determine the parameters of simplified causal models, allowing researchers to estimate cause-and-effect relationships from observed data.

TL;DR

An EM-based algorithm is a two-step iterative process used to find the best parameters for statistical models, particularly useful when some data is unobserved, like in complex causal models.

Key points

Iteratively refines parameter estimates using Expectation (E) and Maximization (M) steps, especially for models with latent variables.
Solves the problem of infeasible parameter estimation in overparameterized causal models (GL-SCMs) from finite observational data.
Used by researchers and ML engineers in causal inference, statistical modeling, and machine learning.
Provides a robust estimation method where direct parameter estimation is often infeasible due to overparameterization or latent variables.
A key research trend in developing robust estimation techniques for complex causal models and latent variable models.

Use cases

Causal Effect Estimation: Estimating the impact of a treatment or intervention from observational data in fields like medicine or economics.
Latent Variable Modeling: Inferring unobserved factors (e.g., personality traits, disease stages) in psychological or medical studies.
Mixture Model Estimation: Identifying distinct subgroups within a population based on observed data, such as customer segmentation.
Missing Data Imputation: Filling in missing values in datasets by modeling the underlying data distribution.
Parameter Learning in Graphical Models: Estimating edge weights and node parameters in Bayesian networks or Markov random fields.

Also known as

EM algorithm, Expectation-Maximization, EM estimation