Edward Kennedy
@edwardhkennedy
Followers
4K
Following
10K
Media
287
Statuses
2K
assoc prof of statistics & data science @CMU_Stats @CarnegieMellon. interested in causality, machine learning, nonparametrics, criminal justice, public policy
Pittsburgh, PA
Joined June 2018
Very excited about this new paper!. We study if one can improve popular semiparametric / doubly robust / DML causal effect estimators -- without adding structural assumptions. Short answer: nope!. Turns out these methods are minimax optimal here. Lots of Qs left to explore.
4
40
202
RT @CarnegieMellon: Taking a break to let Fall do its thing . 🧡. Hope you are enjoying a restful week off, Tartans!. #LifeatCMU
https://t….
0
2
0
Come work with us at CMU! We're hiring tenure-track asst profs in the Dept of Statistics & Data Science.
0
12
38
RT @edwardhkennedy: Charles Stein is one of my all-time favorite statisticians. And this paper from 1956 (!!) is one of my all-time favorit….
0
12
0
RT @edwardhkennedy: A short thread:. It amazes me how many crucial ideas underlying now-popular semiparametrics (aka doubly robust paramete….
0
52
0
RT @StatsSimon: @edwardhkennedy Charles stein actually proposed the crux of TMLE in “efficient non parametric testing and estimation” (1958….
0
2
0
PSA - the main ideas behind “causal ML” and “double machine learning” go back at least 40 years. Here is an estimator from a 1982 textbook that today would be called double machine learning or something similar.
Once you have a pathwise differentiable parameter, a natural estimator is a debiased plug-in, which subtracts off the avg of estimated influence fn. Pfanzagl gives this 1-step estimator here - in causal inference this is exactly the doubly robust / DML estimator you know & love!
2
24
123
RT @ildiazm: Our Division is hosting its inaugural yearly Biostatistics Symposium, and this year the topic is Causal Inference! We have an….
0
12
0
there are surprisingly many open problems when it comes to theory/methods in causal inference. check out this talk by Siva Balakrishnan for an excellent & comprehensive summary of the state of the art.
0
41
224
RT @pedrohcgs: I am loving these new papers on how to select units in an experiment to improve external validity!. This is like taking the….
0
16
0
RT @yanjun_han: I haven’t enjoyed the mathematics in a paper this much in a long time: Summary: an example of perf….
0
20
0
RT @seanjtaylor: These recent slides from Susan Athey and Guido Imbens at NBER are a great recent review of the most valuable data science….
0
63
0
RT @matt_blackwell: Regarding causal inference: Asking people to be specific about what exactly they are trying to estimate and what assump….
0
4
0
RT @edwardhkennedy: Really excited about this paper, w/ amazing postdoc Alex Levis. We propose conditional potenti….
0
7
0
RT @edwardhkennedy: Very excited about this new paper by Tiger Zeng (. We study causal inference w/ high-dimensiona….
0
21
0
RT @edwardhkennedy: check this out if you're interested in connections bw causal & optimal transport. https://t.co/….
0
3
0
check this out if you're interested in connections bw causal & optimal transport.
0
3
20
