The last one I'd say
               
            
            
                07.12.2024 07:41 — 👍 0    🔁 0    💬 0    📌 0                      
            
         
            
        
            
            
            
            
            
    
    
    
    
            It feels like as N increases relative to T, there is a continuum of approaches that starts from treated-unit-specific regularized weights (SC) to global unregularized weights (IPW), and this paper sits somewhere in the middle. Does it make sense?
               
            
            
                06.12.2024 19:22 — 👍 0    🔁 0    💬 1    📌 0                      
            
         
            
        
            
            
            
            
            
    
    
    
    
            Thank you so much! That was super efficient!
               
            
            
                06.12.2024 19:04 — 👍 1    🔁 0    💬 1    📌 0                      
            
         
            
        
            
            
            
            
            
    
    
    
    
            What's the standard approach for synthetic control with many treated units? One at the time? 
If the goal is to match the average outcome in the treatment group over time, why not estimate the propensity score using daily outcomes as covariates, and then build a SC using propensity weights?
               
            
            
                06.12.2024 16:33 — 👍 3    🔁 2    💬 1    📌 0                      
            
         
    
         
        
            
        
                            
                    
                    
                                            Personality psych & causal inference @UniLeipzig. I like all things science, beer, & puns. Even better when combined! Part of http://the100.ci, http://openscience-leipzig.org
                                     
                            
                            
                    
                    
                                            Prof. Most tweets about R. “Polisci, it’s all about what’s going on.” 
http://arelbundock.com
                                     
                            
                    
                    
                                            Fostering a dialogue between industry and academia on causal data science.
Causal Data Science Meeting 2025: causalscience.org
                                     
                            
                    
                    
                                            posts updates from arXiv rss feeds for methodology papers in Statistics and Econometrics. Also maintains an arxiv and posts random papers from it. 
maintainer: @apoorvalal.com
source code: https://github.com/apoorvalal/bsky_paperbot
                                     
                            
                    
                    
                                            Robert H. & Nancy Dedman Trustee Prof of Econ at @SMU, husband, son, proud papa, baseball junkie, animal enthusiast, proudly woke.
http://people.smu.edu/dmillimet/
https://dlm-econometrics.blogspot.com/
                                     
                            
                    
                    
                                            I use mathematics, computation, statistics, & machine learning to help think about biology, engineering, & other things. University of Auckland, NZ. Research: http://tinyurl.com/ojmscholar, Teaching: https://tinyurl.com/ojmteaching
                                     
                            
                    
                    
                                            Machine learning researcher, working on causal inference and healthcare applications
                                     
                            
                    
                    
                                            Biostatistician working on methodology at Novartis. Simulation studies, non-inferiority, missing data, estimands, covariate adjustment…
He/him
https://tpmorris.substack.com/
                                     
                            
                    
                    
                                            postdoc @ stanford econ + incoming assistant prof @ oregon state statistics. networks, causal inference, contagion, measurement error, #rstats. he/him
https://www.alexpghayes.com
                                     
                            
                    
                    
                                            Epidemiologist + Statistician | Clinical Research Facility - University College Cork | UCC School of Public Health | #ClinicalTrials #Epidemiology #Statistics #RStats #WBE #IDSurveillance
Views mine -> https://statsepi.substack.com/
                                     
                            
                    
                    
                                            AI in Bio & Health & Therapeutic Development
Bio: https://linktr.ee/mnarayan
Substack: https://blog.neurostats.org
Peek into my brain: notes.manjarinarayan.org
Previously @dynotx @StanfordMed  PhD@RiceU_ECE | BS@ECEILLINOIS
🧪🧮⚕️🧬🧠🖥🤖📈✍️🩺👩📈📉
                                     
                            
                    
                    
                                            Statistical consultant and programmer at Harvard IQSS. Author/maintainer of the #Rstats packages 'MatchIt', 'WeightIt', and 'cobalt' for causal inference, among many others | He/him
ngreifer.github.io
                                     
                            
                    
                    
                                            Epidemiologist with an interest in causal inference methods at @universityofleeds.bsky.social.
Check out my Intro to Causal Inference Course: https://www.causal.training/
#Epidemiology, #EpiSky, #CausalInference, #CausalSky, #AcademicSky
                                     
                            
                    
                    
                                            Ph.D, stats lover/writer✍🏼, #statistics #scicomm #datascience #statstiktok 👩🏻💻 she/her
                                     
                            
                    
                    
                                            postdoc @nber.org | public, urban, econometrics
https://brad-ross.github.io
                                     
                            
                    
                    
                                            Data Scientist. Poli sci PhD. Cyclist (Gravel, MTB, road). Bayes, causal inference, etc. Also: cats, food opinions. Views & opinions my own. Not investment advice. He/him
                                     
                            
                    
                    
                                            senior lecturer in statistics, penn
NYC & Philadelphia
https://www.stat.berkeley.edu/~winston
                                     
                            
                    
                    
                                            Economics PhD student. Interested in political economy, the media, and the use of ML for causal inference. Trying to do interesting things with interesting data 🏴🇬🇧
                                     
                            
                    
                    
                                            Assistant Professor @JohnsHopkinsAMS, Works in Mathematical Optimization, 
Mostly here to share pretty maths/3D prints, sometimes sharing my research