Rachel Leah Childers on Rachel Leah Childers

Valid Inference with Imperfect Synthetic Data

Papers I Liked 2024

This has been another year where I felt like I slacked on my reading, and that probably is genuinely true for the tumultuous last half, but my read folder lists 154, so I can pick out a few that I liked to highlight in this end-of-year tradition. These are in approximate chronological order by date read, and as always I make no claim to depth or representativeness, so even in areas I follow…

Online Data Collection for Efficient Semiparametric Inference

Timing as an Action: Learning When to Observe and Act

Papers I Liked 2023

I felt like I barely did any serious reading this year, and maybe that’s even true, but my read folder contains 168 papers for 2023, so even subtracting the ones that are in there by mistake, that’s enough to pick a few highlights. As usual, I hesitate to call these favorites, but I learned something from them. They are in no particular order except chronological by when I read them. Themes are…

Local Causal Discovery for Estimating Causal Effects

Differentiable State Space Models and Hamiltonian Monte Carlo Estimation

Automated Solution of Heterogeneous Agent Models

SED 2022 - Notes on contemporary macro

I just got back from an excellent meeting of the Society for Economic Dynamics , a top conference for work in dynamic economics, principally but not exclusively in macroeconomics. As one of the first in-person conferences I’ve been to since 2020 (last year they were hybrid and I presented from home), it was a chance to catch up not just with colleagues and friends but also with the state of modern…

Papers I Liked 2021

A list of 10 papers I read and liked in 2021. As in previous years, this is by date read rather than released or published, and selection is in no particular order. Overall, my list reflects my interests this year, prompted by research and teaching, in online learning, micro-founded macro, and causal inference, and, to the extent possible, intersections of these areas. As usual, I’m likely to have…

Efficient Online Estimation of Causal Effects by Deciding What to Observe

Estimating Treatment Effects with Observed Confounders and Mediators

Top Papers 2020

The following is a look back at my reading for 2020, identifying a totally subjective set of the top 10 papers I read this year. My reading patterns, as usual, have not been so systematic, so if your brilliant work is missing it either slipped past my attention or is living in an ever-expanding set of folders and browser tabs on my to-read list. I’ll exclude papers I refereed, for privacy purposes…

Some issues with Bayesian epistemology

In this post, I’d like to lay out a few questions and concerns I have about Bayesianism and Bayesian decision theory as a normative theory of inductive inference. As a positive theory, of what people do, psychology is full of demonstrations of cases where people do not use Bayesian reasoning (the entire “heuristics and biases” area), which is interesting but not my target. There are no new ideas…

Posterior Samplers for Turing.jl

Prompted by a question on the slack for Turing.jl about when to use which Bayesian sampling algorithms for which kinds of problems, I compiled a quick off-the-cuff summary of my opinions on specific samplers and how and when to use them. Take these with a grain of salt, as I have more experience with some than with others, and in any case the nice thing about a framework like Turing is that you…