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Valid Inference with Imperfect Synthetic Data
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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.
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Online Data Collection for Efficient Semiparametric Inference
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Timing as an Action: Learning When to Observe and Act
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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.
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Local Causal Discovery for Estimating Causal Effects
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Differentiable State Space Models and Hamiltonian Monte Carlo Estimation
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Automated Solution of Heterogeneous Agent Models
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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.
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Papers I Liked 2021
A list of 10 papers I read and liked in 2021.
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Efficient Online Estimation of Causal Effects by Deciding What to Observe
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Estimating Treatment Effects with Observed Confounders and Mediators
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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.
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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.
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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.