# Week #2 – Casual Modeling

Causal modeling is aimed at advancing reasonable hypotheses about underlying causal relationships between the dependent and independent variables.

Consider for example a simple linear model:

 y = a0 + a1 x1 + a2 x2 + e

where y is the dependent variable, x1 and x2 are independent variables, e is the contribution of all other variables and factors. Linear regression analysis allows you to establish the proportion of the variance of y explained by variables x1 and x2 combined.

Methods of causal analysis pretend to partition the combined effect of x1 and x2 into meaningful and mutually exclusive components. Path analysis and analysis of commonality are examples of causal modeling techniques.

Strictly speaking, the actual causal relations cannot be derived unambiguously from such data. The term “causal” should be understood as a metaphor for some mathematical relations between the variables, or as only one of many reasonable models for the actual causal relations.