So far at this conference I have seen reports of true experiments, natural experiments, difference in difference analysis and regression discontinuity design - but no instrumental variable analysis
I wonder why?
I was hoping for the full set of causal inference methods
#statstab #367 Matching in R: Propensity Scores, Weighting (IPTW) and the Double Robust Estimator
Thoughts: A guide on common adjustments for observational studies.
#r #observational #iptw #matching #weights #doublerobust #guide #causalinference
https://www.franciscoyira.com/post/matching-in-r-3-propensity-score-iptw/
What are people’s fave methods for this situation:
At t0, all units are untreated.
As time goes on, individual units are one by one selected for treatment, on an expert’s assessment of their potential improvement under treatment.
How to measure the treatment effect, either over all units or ideally the treatment effect on each unit?
Oh, for extra fun, they’re probably not independent
Registration is open for the GMDS ACADEMY 2025 (Hannover, October 20-23).
There will be three parallel workshops on meta analysis, causal inference and time-to-event analysis involving Wolfgang Viechtbauer (@wviechtb), Christian Röver, Sebastian Weber, Vanessa Didelez, Arthur Allignol, Oliver Kuß, Alexandra Strobel, Hannes Buchner, Xiaofei Liu and Ann-Kathrin Ozga.
See here for more details:
👉 https://www.gmds.de/fileadmin/user_upload/GMDS-Academy-2025.pdf
Causal inference feels like pretty much the most important topic one can think of in statistics or even for humanity in general. So why is the entire field dominated by just one or two people (obviously I'm referring to Judea Pearl and/or Donald Rubin)? It feels rather... cultish.
Can any folks in the field opine why it is so dominated by one or two individuals, compared to any other important area of research today?
#statstab #348 The Effect {book} - Causal Diagrams
Thoughts: At some point you'll need to learn about DAGs. Maybe this is the chapter you need.
Postdoc in Single-Cell Multi-Omic Gene Regulatory Networks
University of Massachusetts Chan Medical School
Join us to decode #GeneRegulatoryNetwork from #SingleCell multiomics with #CausalInference as a #postdoc! Quantitative bg needed.
See the full job description on jobRxiv: https://jobrxiv.org/job/university-of-massachusetts-chan-medica...
https://jobrxiv.org/job/university-of-massachusetts-chan-medical-school-27778-postdoc-in-single-cell-multi-omic-gene-regulatory-networks/?feed_id=94702
Hello SFBA! I’ve been wistfully thinking of switching over here for a while and recent fosstodon choices gave me the push I needed. So #introduction time!
I’m from #SanFrancisco and moved back here after some wandering. Raising two kids and a dog. Working in tech (sigh) but on #sustainability at least.
Interested in and post about #CausalInference, #Statistics, #Politics, #Policy, #Climate, #Energy, #Dogs, #Crafting and #Parenting
Postdoc in Single-Cell Multi-Omic Gene Regulatory Networks
University of Massachusetts Chan Medical School
Join us to decode #GeneRegulatoryNetwork from #SingleCell multiomics with #CausalInference as a #postdoc! Quantitative bg needed.
See the full job description on jobRxiv: https://jobrxiv.org/job/university-of-massachusetts-chan-medica...
https://jobrxiv.org/job/university-of-massachusetts-chan-medical-school-27778-postdoc-in-single-cell-multi-omic-gene-regulatory-networks/?feed_id=94125
This looks great: Andrew Gelman (@statmodeling_bot ) would be joining Nancy Cartwright and Berna Devezer. Short idea talks, lots of panel discussion and Q&A.
Join us on April 25th to discuss RCTs, replications, and scientific inference.
https://sites.google.com/view/cepbi/talks-gatherings?authuser=0
The case for multiple UESDs and an application to migrant deaths in the Mediterranean Sea https://doi.org/10.1017/psrm.2025.17 #CausalInference Analyzing multiple, comparable unexpected events happening during survey data collection makes a lot of sense to assess patterns. In doing so, one has to follow 1/
Postdoc in Single-Cell Multi-Omic Gene Regulatory Networks
University of Massachusetts Chan Medical School
Join us to decode #GeneRegulatoryNetwork from #SingleCell multiomics with #CausalInference as a #postdoc! Quantitative bg needed.
See the full job description on jobRxiv: https://jobrxiv.org/job/university-of-massachusetts-chan-medica...
https://jobrxiv.org/job/university-of-massachusetts-chan-medical-school-27778-postdoc-in-single-cell-multi-omic-gene-regulatory-networks/?feed_id=93671
#statstab #307 The C-word, the P-word, and realism in epidemiology
Thoughts: A comment on #306. Causal inference in observational research is a confusing matter. Read both.
#causalinference #observational #research #commentary
https://link.springer.com/article/10.1007/s11229-019-02169-x
#statstab #306 The C-Word: Scientific Euphemisms Do Not Improve Causal Inference From Observational Data
Thoughts: Causal inference is messy business. Maybe we need to be more honest about that.
Postdoc in Single-Cell Multi-Omic Gene Regulatory Networks
University of Massachusetts Chan Medical School
Join us to decode #GeneRegulatoryNetwork from #SingleCell multiomics with #CausalInference as a #postdoc! Quantitative bg needed.
See the full job description on jobRxiv: https://jobrxiv.org/job/university-of-massachusetts-chan-medica...
https://jobrxiv.org/job/university-of-massachusetts-chan-medical-school-27778-postdoc-in-single-cell-multi-omic-gene-regulatory-networks/?feed_id=93482
Postdoc in Single-Cell Multi-Omic Gene Regulatory Networks
University of Massachusetts Chan Medical School
Join us to decode #GeneRegulatoryNetwork from #SingleCell multiomics with #CausalInference as a #postdoc! Quantitative bg needed.
See the full job description on jobRxiv: https://jobrxiv.org/job/university-of-massachusetts-chan-medica...
https://jobrxiv.org/job/university-of-massachusetts-chan-medical-school-27778-postdoc-in-single-cell-multi-omic-gene-regulatory-networks/?feed_id=93328
Postdoc in Single-Cell Multi-Omic Gene Regulatory Networks
University of Massachusetts Chan Medical School
Join us to decode #GeneRegulatoryNetwork from #SingleCell multiomics with #CausalInference as a #postdoc! Quantitative bg needed.
See the full job description on jobRxiv: https://jobrxiv.org/job/university-of-massachusetts-chan-medica...
https://jobrxiv.org/job/university-of-massachusetts-chan-medical-school-27778-postdoc-in-single-cell-multi-omic-gene-regulatory-networks/?feed_id=92866
@lobsters All this pervasive “least correlation between time invested and size of resulting product” along with the “solutions that must be maintained in a changing world” inevitably ring a bell about the out-of-distribution problems that typically arise under the influence of actionable inputs. #causalinference can possibly be helpful in this context.
@newsycombinator An illustrative example of collider bias: Location -> Restaurant Success <- Food Quality
I recall that @rlmcelreath used it in one of his lectures… It is surprising how many students of #causalinference (mostly the ones that overtly reject DAGs as a useful source of knowledge representation) miss this effect. I vividly recall how Donald Rubin himself told me that he had never seen any colliders in the real world while answering questions after a lecture at Northwestern. ¯\_(ツ)_/¯
Postdoc in Single-Cell Multi-Omic Gene Regulatory Networks
University of Massachusetts Chan Medical School
Join us to decode #GeneRegulatoryNetwork from #SingleCell multiomics with #CausalInference as a #postdoc! Quantitative bg needed.
See the full job description on jobRxiv: https://jobrxiv.org/job/university-of-massachusetts-chan-medica...
https://jobrxiv.org/job/university-of-massachusetts-chan-medical-school-27778-postdoc-in-single-cell-multi-omic-gene-regulatory-networks/?feed_id=92495
