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I am trying to compare a database of campaign finance contributions to candidates with a database of congressional votes and see if I can unearth any causal relationships between campaign finance sources and legislative action.

Causal relationships are difficult to establish of course, because it is not necessarily obvious whether a donor is supporting a candidate who agrees with their policy objectives, or is offering money to change a candidate's policy objectives. There are many many practical difficulties with determining the sources of campaign financing, which I will ignore for the purposes of this question.

Therefore, my primary assumption is that campaign finance contributions from a given source would have a strong correlation to a certain voting block for a piece of legislation relevant to their interests.

Suppose we take a list of campaign contributions from the following PACs to federal congressmen and senators:

  • American Healthcare Association PAC
  • Boeing PAC
  • AFLAC PAC
  • GlaxoSmithKline PAC
  • American Academy of Family Physicians PAC (FamMedPAC)
  • Eli Lilly and Company PAC

  • Lockheed Martin Employees PAC And we run an analysis of all congressmen and senators' votes on only one piece of legislation, something healthcare-related like the Affordable Care Act. We would expect that Lockheed Martin and Boeing's contributions to have a comparatively little causal relationship on a representatives' vote, and the rest of the PACs (which are healthcare-related) would have a strong relationship, either with a vote for, or a vote against, the legislation.

How would you run this analysis? Linear Regression seems silly because a vote is a binary yes/no value.

  • Can you be more specific about exactly what causal relationship you are testing? The question as written sounds more like exploratory research rather than testing a causal hypothesis. And I'm not sure what you mean by "unearth" causal relationships. – birch Oct 17 '18 at 1:54

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