#statstab #505 Beyond Confounders

Thoughts: What makes a good control and a bad control?

#counterfactuals #confounder #DAG #r #modelling #selectionbias #variance #control #causalinference

matheusfacure.github.io/python-causa...

Paper link here! 13/13
papers.ssrn.com/sol3/papers....

#econsky #polisky #MetaScience #OpenScience #CausalInference #StatsTwitter #Econometrics #AcademicSky

Target Trial Emulation (TTE). Dates: June 8-12, 2026. Taught by Barbra Dickerman, Joy Shi, Miguel Hernán

Interested in using health databases for #causalinference research?

Target Trial Emulation (TTE) covers the target trial emulation framework in increasingly complex settings.

📆 June 8-12, 2026

Taught by Babra Dickerman, Joy Shi, @miguelhernan.org

Apply now:
hsph.harvard.edu/research/cau...

Debiased Front-Door Learners for Heterogeneous Effects In observational settings where treatment and outcome share unmeasured confounders but an observed mediator remains unconfounded, the front-door (FD) adjustment identifies causal effects through the m...

Our paper “Debiased Front-Door Learners for Heterogeneous Effects” was accepted to ICLR 2026.

- Paper (arXiv): arxiv.org/abs/2509.22531
- Reproducible code: github.com/yonghanjung/...

Quick start:
pip install fd-cate
fdcate demo --outdir ./fdcate-demo
#ICLR2026 #CausalInference #MachineLearning

Most quant models are correlational - they tell you what moved together in the past.

But robust investing needs more than correlation. It needs causal structure + functional form.

Our latest blog explores how the two work together.

👉 Read more: dub.link/Xe9cHWg

#CausalInference #QuantFinance

Does anyone out there have a syllabus for a causal inference course targeting senior undergrad or early grad students? ##AcademicSky #CausalInference

Melody Huang (Yale) | Applied Statistics Workshop Gov 3009 Breadcrumbs

Weds at 12:00 ET: #Yale assistant professor @melodyyhuang.bsky.social presents "Relative Bias Under Imperfect Identification in Observational #CausalInference" at this week's #AppliedStatistics workshop. #politicalscience #statistics
appliedstatsworkshopgov3009.hsites.harvard.edu/event/melody...

Sage Journals: Discover world-class research Subscription and open access journals from Sage, the world's leading independent academic publisher.

#statstab #497 On the Statistical Analysis of Experiments
With Manipulation Checks

Thoughts: All psychologists reading this title will panic. Yes, you can't just delete data and assume all is well.

#assumptions #QRPs #estimand #causalinference #ITT #ATE #bias

journals.sagepub.com/doi/pdf/10.1...

Causal questions need causal tools. EcoTwin explains how the do operator and causal graphs help predict the effects of marine interventions.

ecotwinproject.eu/post/the-do-...

#EcoTwin #CausalInference #OceanPolicy

Royal Statistical Society Publications Summary. Conventional analytic results do not reflect any source of uncertainty other than random error, and as a result readers must rely on informal judgments regarding the effect of possible bias...

So does this one:

rss.onlinelibrary.wiley.com/doi/abs/10.1...

#causalsky #causalinference

Sophia Rein new role: Instructor of Epidemiology

Congratulations to CAUSALab researcher Sophia Rein for her promotion to Instructor of Epidemiology!

Thank you for all your incredible work, Sophia.

@harvardepi.bsky.social #causalinference #publichealth #hsph #epidemiology

Week 23: Building a Causal Effect VAE for Health Equity Week 23: Building a Causal Effect VAE for Health Equity *Cracks hands* Let’s get this Causal Effect VAE for Health Equity (CEVAE-HE) working! Quick reminder about our goal: we are aiming to …

Week 23: Building a Causal Effect VAE for Health Equity

We're not quite there yet to get it to do its job properly, but we'll get there!

medium.com/retraining-e...

#AI #research #healthcare #causalinference #socialjustice

Stratified Causal Inference for Intensive Care Unit Risk Prediction: Informatics-Based Modeling of Anesthetic Drug Combinations Background: Postoperative intensive care unit (ICU) admission affects 15% to 20% of surgical patients and represents a major source of morbidity and health care costs. Current anesthetic dosing relies on empirical guidelines rather than individualized risk assessment. We developed a counterfactual dose-response model to identify optimal fentanyl-propofol combinations. Objective: This study aimed to develop and evaluate a stratified, causal machine learning framework using electronic health record data to identify optimal fentanyl-propofol dose combinations and predict postoperative ICU admission risk, enabling precision anesthesia and individualized clinical decision support. Methods: We analyzed perioperative electronic health records of 67,134 surgical procedures from UC Irvine Medical Center (2017‐2022). A hierarchical learning framework was used to estimate causal effects while controlling for confounding variables. A total of 6 dose-sensitive subgroups were identified through stratified analysis. The primary end point was postoperative ICU admission. Results: High-risk combinations (fentanyl >5 mcg/kg with propofol

JMIR Formative Res: Stratified Causal Inference for Intensive Care Unit Risk Prediction: Informatics-Based Modeling of Anesthetic Drug Combinations #CausalInference #MachineLearning #Anesthesia #ICURiskPrediction #HealthcareInnovation

Common statistical and causal assumptions used for valid causal inference from data.

Text from the supplement re: causal v. statistical assumptions

One thing that will really help folk is in the supplement - what is a causal assumption and what is the difference between a statistical assumption and a causal assumption. static-content.springer.com/esm/art%3A10... #causalinference 🌍🧪

There's also a ton more in the supplement that is useful!

And an amazing #causalinference in #ecology team beyond Hannah & Paul to think & grow with - @lauradee.bsky.social, @fiebergjohn.bsky.social , Marie-Josée Fortin, Clark Glymour, @jakobrunge.bsky.social, Bill Shipley, Ilya Shpitser, @katherinesiegel.bsky.social, George Sugihara, & Betsy von Holle

The workflow illustrates a step-by-step process for conducting causal analyses. Arrows indicate the typical flow of an analysis. Two possible pathways are shown: causal discovery approaches (blue), which aim to identify the existence of causal relationships when pre-existing knowledge is low, and causal inference approaches (yellow), which aim to quantify the direction and magnitude of causal effects when pre-existing knowledge is high. The gray feedback loop on the right highlights the iterative refinement of causal analyses based on assessments of the plausibility of causal assumptions.

So, y'all have heard me going on about #causalinference in #ecology a lot. Now our big synthetic guide "Best practices for moving from correlation to causation in ecological research" is out! Led by Hannah Correia & Paul Ferraro, it's a great walk-through for all! 🌍🧪 www.nature.com/articles/s41...

New #causalinference paper just dropped! As an ecologist, I was trained to ask: "What do the data tell me?"

This paper: there are only specific instances when this question is appropriate—when you lack domain knowledge, which we often have!

www.nature.com/articles/s41...

Ecopath with Ecosim as a causal model… how its trophic networks align naturally with causal inference.

Read More: ecotwinproject.eu/post/ecosyst...

#EcoTwin #MarineScience #CausalInference #HorizonEurope

📣The 2026 Symposium of #CausalInference in the #HealthSciences takes place on March 18, 2026 in Fribourg. Theme: AI & machine learning in causal inference for health sciences
🎤Keynotes: Elsa Gautrain, Aurélien Sallin, Jonas Peters, Jana Mareckova
🔗https://projects.unifr.ch/pophealthlab/?page_id=1561

GitHub - danielegirardi/lpdid: Implementing Local Projections Difference-in-Differences (LP-DiD) estimators Implementing Local Projections Difference-in-Differences (LP-DiD) estimators - danielegirardi/lpdid

📢 PSA for #EconSky & #RStats:

The GitHub repo for our Local Projections Difference-in-Differences (LP-DiD) paper now has example scripts for R!

If you're looking to implement LP-DiD in R, these are for you

Here: github.com/danielegirar...

#Econometrics #CausalInference

@arindube.bsky.social

"Mediators, confounders, colliders – a crash course in causal inference"
by Florian Hartig (2019): theoreticalecology.wordpress.com/2019/04/14/mediators-con...

#offPolicy #causality #causalInference #stats #statistics #counterFactuals […]

adrftools: Estimating, Visualizing, and Testing Average Dose-Response Functions Facilitates estimating, visualizing, and testing average dose-response functions (ADRFs) for characterizing the causal effect of a continuous (i.e., non-discrete) treatment or exposure. Includes suppo...

I'm so excited to announce the first release of my newest #Rstats package, {adrftools}! This package facilitates estimation, visualization, and testing for the causal effect of a continuous (i.e., non-discrete) treatment.

🧵 1/10

#statssky #episky #causalinference

#statstab #485 Bayesian ANCOVA and the ATE

Thoughts: Still grappling with the implications of using the causal inference approach to randomized experiments. But it's interesting.

#ATE #causalinference #ancova #ANOVA #rstats #estimand #counterfactuals

solomonkurz.netlify.app/blog/2025-07...

Yeah Y'all know what this is.

#Zajey #Science #Research #ScientificMethod #SystemsThinking #Causality #CausalInference #DecisionScience #ComplexityScience #ComplexSystems #NonlinearDynamics #ChaosTheory #Probability #Statistics #Uncertainty #RiskAnalysis #Modeling #Inference
2/2

#Bayesian causal inference is about generative mechanisms, not just correlations.

Learn how to encode causal assumptions and simulate counterfactuals in our Applied Bayesian Regression Modeling course.

Registration still open 👇
dub.link/PKrUYFP

#PyMC #CausalInference

2026 CAUSALab Summer Courses on Causal Inference: KTCI, TTE, CICI, ACA. Online prerequisite course: FCA.

See you in June? ☀️

Applications for the 2026 Summer Courses on Causal Inference are open! Join CAUSALab this summer to learn from the #causalinference experts on campus
@hsph.harvard.edu or online.

Learn more & secure your spot today:
hsph.harvard.edu/research/cau...

Introducing Causion: A web app for playing with DAGs | Peder M. Isager Personal website of Dr. Peder M. Isager

#statstab #482 Introducing Causion: A web app for playing with DAGs

Thoughts: A very cool app. Let's you see exactly what your assumptions and DGP mean for your causal model.

#causal #causalinference #DAG #DAGs #dgp #tutorial #guide #education #pedagogy

pedermisager.org/blog/causion...

Orthogonal Representation Learning for Estimating Causal Quantities

#CausalInference

https://arxiv.org/pdf/2502.04274

New on JCRE: Andrew Gelman and Guido Imbens reply to Melle R. Albada’s comment on Regression Discontinuity Designs. #CausalInference #EconSky Read the exchange here: jcr-econ.org/stable-adjus...

21st Kolokotrones Symposium - April 3, 2026

Save the date for the 21st Kolokotrones Symposium! 📌

📆 April 3, 2026
📍 In-person @hsph.harvard.edu or online

Spring symposium details to be announced in the coming weeks. #causalinference #publichealth