A brief thread rant on woodworking and causal inference (yeah, you read that right).
From table legs to descriptive stats tables, from picture frames to the framing the big picture for studies. It's gonna get weird, but stick with me.
Let's say you want to make a very simple table. Easy! 4 legs cut to the same length and flat top. Step 1: cut those legs.
So, you take your leg material, and you carefully measure (twice) 26," mark it, and make your cut.
And no matter how careful you were, they don't match.
You might think that you didn't measure carefully enough, or cut straight enough. I promise that's not the problem.
The problem is that you were thinking about the problem the wrong way. Because unless you are a pro, measure twice cut once will NEVER get them to match.
Measure twice cut once (MTCO) is the solution if you want all 4 legs to be as close to 26" as you can. But that's not actually what you want.
What you want is that all 4 legs are exactly EQUAL to each other somewhere around 26." You have the wrong objective.
In woodwork, the general solution to this is to make a quick "jig" (something to help you make a cut). Put a block ~26" from saw blade, put leg #1 against the stop block, make your cut, and repeat, without moving the stop block.
Boom, 4 legs, exactly equal.
In causal inference, we are dealing with a very similar problem. We have a TON of variables we have to deal with, where any one particular thing throws off our results.
One solution is to very, very carefully examine the problem and map (measure) it out.
You think about all the variables and how they all fit together and how to control for them. Can often be done with the right skills and tools, but it's really hard.
I prefer to think about things a bit differently, and set things up so that I never have to measure.
That's the general idea behind a lot of the methods in what I like to call the avoidance by design strategy for causal inference.
The biggest of these strategies is the RCT. But back to wood for a second.
Time for picture frames.
Picture frames are usually 4 pieces of wood, where the ends are cut to 45 degrees. When you put it together, it's a rectangle.
You can try and measure out and trace 45 degrees, but again, you failed before you started. Wrong goal, wrong method.
Instead, one way to "fix" things is cutting against a jig that you know is 90 degrees, turned so it's ~45 degrees from your cat. Cut one side of the joint on one side of that jig, and the other side on the other.
Even if your jig isn't exactly 45, the two errors cancel out.
That's the miracle of the randomized controlled trial: you expect the all of those issues to cancel each other out and become statistical error. Statistical error is WAY better than the more common use of the term error.
You might think, well, maybe I should check?
Let's see if the sides (trial arms) are equally 45 degrees (or demograhics or whatever), and test it in table 1 (see what I did there) with a p-value.
But that's the wrong objective, because you don't actually care about that; it doesn't really matter if they're off.
You've set the system up (randomization) so that you can get the right answer even if things look wonky when you measure them.
Measuring closer to 45 (analogous to carefully controlling for baseline vars) gets you some extra precision, but that's just bonus points.
Sometimes, you have to measure and there is no clever way around it through an RCT, instrument, or whatever, and you just have to measure. And sometimes jigs don't work like you think they should.
That can be fine, depending on what you're building.
Personally, I avoid measuring always, just like I avoid controlling for stuff. I really really don't like it when tables are all wobbly,, and I'd rather set myself up for success.
Sometimes that means I can't always build the thing I want to. That's ok too.
Maybe don't use a cat as a reference, but an illustration of what I mean here. Put piece along horiz line. Cut half of the joint along the left part of the 90 degrees, the other half on the other. It'll end up 90 even if the box isn't 45 to the line.
Now why is this important? Well, COVID treatments for one.
This is the idea behind why we REALLY need to get good, solid RCT evidence for treatments and vaccines. In those cases, measuring/controlling just ain't gonna cut it, and is one of the reasons HCQ is/was such a debacle.
• • •
Missing some Tweet in this thread? You can try to
force a refresh
Folks often say that DAGs make our causal inference assumptions explicit. But that's only kinda true
The biggest assumptions in a DAG aren't actually IN the DAG; they're in what we assume ISN'T in the DAG. It's all the stuff that's hidden in the white space.
Time to make it official: short of some unbelievably unlikely circumstances, my academic career is over.
I have officially quit/failed/torpedoed/given up hope on/been failed by the academic system and a career within it.
To be honest, I am angry about it, and have been for years. Enough so that I took a moonshot a few years ago to do something different that might change things or fail trying, publicly.
I could afford to fail since I have unusually awesome outside options.
And here we are.
Who knows what combination of things did me in; incredibly unlucky timing, not fitting in boxes, less "productivity," lack of talent, etc.
In the end, I was rejected from 100% of my TT job and major grant applications.
Always had support from people, but not institutions.
Ever wondered what words are commonly used to link exposures and outcomes in health/med/epi studies? How strongly language implies causality? How strongly studies hint at causality in other ways?
READ ON!
Health/med/epi studies commonly avoid using "causal" language for non-RCTs to link exposures and outcomes, under the assumption that ""non-causal"" language is more ""careful.""
But this gets murky, particularly if we want to inform causal q's but use "non-causal" language.
To find answers, and we did a kinda bonkers thing:
GIANT MEGA INTERDISCIPLANARY COLLABORATION LANGUAGE REVIEW
As if that wasn't enough, we also tried to push the boundaries on open science, in hyper transparency and public engagement mode.
Granted, we only see the ones that get caught, so "better" frauds are harder to see.
But I think people don't appreciate just how hard it is to make simulated data that don't have an obvious tell, usually because somethig is "too clean" (e.g. the uniform distribution here).
At some point, it's just easier to actually collect the data for real.
BUT.
The ones that I think are going to be particularly hard to catch are the ones that are *mostly* real but fudged a little haphazardly.
Perpetual reminder: cases going up when there are NPIs (e.g. stay at home orders) in place generally does not tell us much about the impact of the NPIs.
Lots of folks out there making claims based on reading tea leaves from this kind of data and shallow analysis; be careful.
What we want to know is what would have happened if the NPIs were not there. That's EXTREMELY tricky.
How tricky? Well, we would usually expect case/hospitalizations/deaths to have an upward trajectory *even if when the NPIs are extremely effective at preventing those outcomes.*
The interplay of timing, infectious disease dynamics, social changes, data, etc. make it really really difficult to isolate what the NPIs are doing alongside the myriad of other stuff that is happening.
The resistance to teaching regression discontinuity as a standard method in epi continues to be baffling.
I can't think of a field for which RDD is a more obviously good fit than epi/medicine.
It's honestly a MUCH better fit for epi and medicine than econ, since healthcare and medicine are just absolutely crawling with arbitrary threshold-based decision metrics.
(psssssst to epi departments: if you want this capability natively for your students and postdocs - and you absolutely do - you should probably hire people with cross-disciplanary training to support it)