Causal Inference and Structural Equations

This set of flashcards covers key vocabulary and concepts related to causal relationships, structural equations, and analysis in econometrics.

Predictive Models

Models that are descriptive and based on associations for forecasting outcomes.

Causal Models

Models that are explanatory and based on interventions to determine the effect of variables.

Structural Equation

An equation that describes how one variable is affected by one or more other variables, including an error term.

Potential Outcome

The value of an outcome variable that could occur if a treatment were applied, often denoted as Y(x).

Ceteris Paribus

A Latin phrase meaning 'all else being equal', used to analyze the causal effect of one variable on another.

Exogeneity

A condition where the error term is independent of the explanatory variables, implying that the variable does not cause any error.

Average Causal Effect (ACE)

The expected effect of an intervention on the outcome, calculated by comparing potential outcomes under different scenarios.

Average Marginal Effect (AME)

The expected change in the outcome variable for a one-unit change in a specific explanatory variable, holding other variables constant.

Randomized Clinical Trials (RCTs)

Experiments where subjects are randomly assigned to treatment or control groups to ensure that treatment assignment does not cause bias.

Interactive Linear Model

A model that includes interaction terms to evaluate how the effect of one variable changes depending on the level of another variable.