LICENSE

* MIT License

Copyright (c) [2020] [Anthony Scott Cunningham]

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, and/or distribute copies of the course
materials, and to permit persons to whom the class materials is furnished to do so, 
subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.


PERSONAL MANIFESTO

I created a causal inference and research design course for Baylor University and workshops because I wanted to learn the modern
area of applied microeconometrics and the philosophical foundations of causality. This beamer deck, complete with the graphics, 
and the accompanying R and Stata programs, are the final product of that effort. These files are free to anyone who wants to use them. 
You also can alter them, remove material, add material and whatever else you want without asking for permission. My priority is 
to lower the fixed and marginal costs associated with people developing this course themselves, which i think is easier if 
someone gives you their 1000 slides, and I encourage you to consider developing these materials into a course for your students.

The audience for this course is:

- upper level undergrads
- masters students
- entering PhD students, probably as a bootcamp model in the summer prior to entry

I maintain the position until I believe otherwise that teaching causal inference out of order, before econometrics, 
helps students who then take econometrics learn the econometric material better. Not because causal inference is synonymous 
with econometrics, either. It very much isn't. Rather, causal inference pins down via the potential outcomes notation and a 
focus on research design concepts that students the concept of causality which can help students have a simple crutch to 
take with them in their econometrics classes. 

There's some estimators obviously in a causal inference course -- namely various applications of propensity scores, twoway 
fixed effects, instrumental variables estimators like two stage least squares and JIVE, and synthetic control. But these are 
often fairly basic and econometrics fleshes them out. My point is these are complements, not substitutes, and causal inference 
in the Neyman tradition as well as some of these Pearl-Wright graphical tools can really help a student have a heuristic to 
latch onto econometrics, even if in the end they let go of that crutch. AT least I think this is true for the marginal student 
who feels lost in econometrics. This class in my humble opinion can help them just by putting a rung a little lower down.

Anyway, that's my pedagogical conviction, and here's the class if you want it. Like I said, take it and do with it whatever you 
want, but it follows pretty closely my updated Yale book minus Bartik instruments, which I do cover in the Mixtape, but which 
I want a little more time to beef up for the class.