# What are the best methods for analyzing: X, Y, and Z change over time, and I want to see whether X and Y cause changes in Z

I have a data-set, and the details are not crucial, but if it helps: The variables are (A) country, (B) investment, (C) campaign type, (D) external event. Obviously A is a categorical variable, B is a continuous quantitative variable, C is categorical, and D is a Bernoulli variable. I also have information about these for each year stretching back decades. I would like to know whether A, C, and D cause changes in B and exactly what that causal relationship is if it exists.

So my question is, how should I analyze this data, in broad terms? Should I use Time Series Analysis? It sounds like this should be a time-series problem but given that I've never studied the subject, I'm not certain. Is it possible to derive causal information from this data given that it's observational? It would be particularly great if there were some textbook or other resource that I could read to learn about the tools I ought to be using in a case like this.

Regression with categorical variables is relatively simple in R using lm() with factors() to convert categorical variables to leveled factors.
Here you would regress using something a model like this... $B \sim \alpha + \beta_1 A - \beta_2 C + \beta_3 D + \epsilon$