Emily Riederer
@emilyriederer.bsky.social
Here for data, data science, analytics engineering, rstats, books
Mostly a review of pretty standard methods, but some fun examples of enabling expression expansion (the magic behind column selectors!), more complex objects in a dataframe (models, vectors), and breaking the paradigm to go back to partitioned dataframes + list comprehensions
(2/2)
(2/2)
November 16, 2025 at 4:15 PM
Mostly a review of pretty standard methods, but some fun examples of enabling expression expansion (the magic behind column selectors!), more complex objects in a dataframe (models, vectors), and breaking the paradigm to go back to partitioned dataframes + list comprehensions
(2/2)
(2/2)
Maybe this is an “when I was a kid” thing, but book stores (ok definitely a “when I was a kid” thing) always used to have those “page a day” tear off calendars
November 15, 2025 at 5:37 PM
Maybe this is an “when I was a kid” thing, but book stores (ok definitely a “when I was a kid” thing) always used to have those “page a day” tear off calendars
Counting down to day 2, coming at me starting 3:30A Chicago time! 🤓☕
(Also, massive respect to the folks in the chat who mentioned joining from Seattle time 😴)
7/7
(Also, massive respect to the folks in the chat who mentioned joining from Seattle time 😴)
7/7
November 13, 2025 at 12:18 AM
Counting down to day 2, coming at me starting 3:30A Chicago time! 🤓☕
(Also, massive respect to the folks in the chat who mentioned joining from Seattle time 😴)
7/7
(Also, massive respect to the folks in the chat who mentioned joining from Seattle time 😴)
7/7
Not enough overlap to estimate ATE with potential outcomes? Elegant approaches for finding bounds when you can't point estimate arxiv.org/abs/2509.20206
Reminds me of some talks from last year aiming to rank when they couldn't estimate. Lots of analytically useful outcomes beyond estimates
(6/n)
Reminds me of some talks from last year aiming to rank when they couldn't estimate. Lots of analytically useful outcomes beyond estimates
(6/n)
Non-overlap Average Treatment Effect Bounds
The average treatment effect (ATE), the mean difference in potential outcomes under treatment and control, is a canonical causal effect. Overlap, which says that all subjects have non-zero probability...
arxiv.org
November 13, 2025 at 12:17 AM
Not enough overlap to estimate ATE with potential outcomes? Elegant approaches for finding bounds when you can't point estimate arxiv.org/abs/2509.20206
Reminds me of some talks from last year aiming to rank when they couldn't estimate. Lots of analytically useful outcomes beyond estimates
(6/n)
Reminds me of some talks from last year aiming to rank when they couldn't estimate. Lots of analytically useful outcomes beyond estimates
(6/n)
Survey of models in APSR papers, using DAGs to analyze main model described and identify incorrect controlling for colliders, mediators, etc. propping up "significant" results mikedenly.com/research/dags
(5/n)
(5/n)
DAG It: Drawing Assumptions Before Conclusions Changes Results
mikedenly.com
November 13, 2025 at 12:16 AM
Survey of models in APSR papers, using DAGs to analyze main model described and identify incorrect controlling for colliders, mediators, etc. propping up "significant" results mikedenly.com/research/dags
(5/n)
(5/n)
Speaking of robust simulations, great talk on quantifying the impact of different estimators and policy learning approaches through simulation with applications in public health (team behind this paper but can't find the actual paper arxiv.org/abs/2501.12803)
(4/n)
(4/n)
Exploring the heterogeneous impacts of Indonesia's conditional cash transfer scheme (PKH) on maternal health care utilisation using instrumental causal forests
This paper uses instrumental causal forests, a novel machine learning method, to explore the treatment effect heterogeneity of Indonesia's conditional cash transfer scheme on maternal health care util...
arxiv.org
November 13, 2025 at 12:16 AM
Speaking of robust simulations, great talk on quantifying the impact of different estimators and policy learning approaches through simulation with applications in public health (team behind this paper but can't find the actual paper arxiv.org/abs/2501.12803)
(4/n)
(4/n)
No more thoughtless logistic regressions! Very intuitively, correctly calibrating propensity scores can improve bias *and* variance of casual estimates arxiv.org/abs/2503.17290
(3/n)
(3/n)
Calibration Strategies for Robust Causal Estimation: Theoretical and Empirical Insights on Propensity Score-Based Estimators
The partitioning of data for estimation and calibration critically impacts the performance of propensity score based estimators like inverse probability weighting (IPW) and double/debiased machine lea...
arxiv.org
November 13, 2025 at 12:16 AM
No more thoughtless logistic regressions! Very intuitively, correctly calibrating propensity scores can improve bias *and* variance of casual estimates arxiv.org/abs/2503.17290
(3/n)
(3/n)
Survey of causal method evaluation and areas of opportunity -- focused on more credible benchmarks and simulations www.arxiv.org/abs/2508.08883
(2/n)
(2/n)
Position: Causal Machine Learning Requires Rigorous Synthetic Experiments for Broader Adoption
Causal machine learning has the potential to revolutionize decision-making by combining the predictive power of machine learning algorithms with the theory of causal inference. However, these methods ...
www.arxiv.org
November 13, 2025 at 12:15 AM
Survey of causal method evaluation and areas of opportunity -- focused on more credible benchmarks and simulations www.arxiv.org/abs/2508.08883
(2/n)
(2/n)
The way you could ask a random question and the most qualified person in the world would just stop by to answer
Or how someone would ask the one thing you asked yourself last week and had the perfect link
Or how someone would ask the one thing you asked yourself last week and had the perfect link
November 9, 2025 at 10:31 PM
The way you could ask a random question and the most qualified person in the world would just stop by to answer
Or how someone would ask the one thing you asked yourself last week and had the perfect link
Or how someone would ask the one thing you asked yourself last week and had the perfect link