New episode is out, my dear Bayesians! All about #CausalInference, #Experimentation at scale, and #GaussianProcesses -- definitely a fun one!

Bayesian Causal Inference at Scale Thomas Pinder discusses Bayesian causal inference and Gaussian processes. Explore synthetic control and diff-in-diff for industry

New Episode Alert!
🎙️ Scaling #BayesianCausalInference with Thomas Pinder, Netflix & creator of GPJax

Essential listening for anyone working at the frontier of Bayes, Experimentation & Causal Inference 📈
🔗 learnbayesstats.com/episode/154-...

#Bayesian #JAX #MachineLearning #CausalInference #GPJax

Original post on hachyderm.io

"Five skills. Each one is counter-cyclical (becomes more valuable as hype recedes), resistant to LLM automation (requires human judgment that pattern-matching can’t replicate), and directly tied to the business outcomes executives actually pay for."
by Kaushik Rajan […]

Apply now: T32 Postdoctoral Research Fellow

Searching for a postdoc opportunity?

CAUSALab is reviewing applications for the T32 Training Program in Comparative Effectiveness Research for Suicide Prevention (Funded by NIMH, T32 MH125815).

🔗 Apply:
form.jotform.com/201476846966...

Learn more in comments. #causalinference

Graph Neural Networks can help reveal causal relationships in marine systems.

ecotwinproject.eu/post/graph-n...

#EcoTwin #AI #CausalInference #DigitalTwins

Theory for Identification and Inference with Synthetic Controls: A Proximal Causal Inference Framework Synthetic control (SC) methods are commonly used to estimate the treatment effect on a single treated unit in panel data settings. An SC is a weighted average of control units built to match the tr...

www.tandfonline.com/doi/full/10....

#causalsky #causalinference #StatsSky

Individualized Dynamic Mediation Analysis Using Latent Factor Models Mediation analysis plays a crucial role in causal inference as it can investigate the pathways through which treatment influences outcome. Most existing mediation analysis assumes that mediation ef...

www.tandfonline.com/doi/full/10....

#CausalSky #causalinference #statssky

Causal Inference Certification | Causal Training | Statistical Horizons Causal inference certification for researchers seeking applied training & a credential in causal methods. Complete four live online seminars.

Get certified in #causalinference. Across 4 seminars, you’ll learn how to think more clearly about design, estimation, assumptions, and interpretation in applied settings. Strengthen your research toolkit and earn a credential in the process.

Online course on Causal Inference using an SEM Approach from June 1-5, 2026 with registration link.

Ready to level up your research skills? 🚀 "Causal Inference: An SEM Approach" covers #CausalInference, #PathAnalysis, #SEMs, and #Econometrics—all in one workshop! Sign up now: myumi.ch/158dw

#SumProg26 #ICPSR #GraduateStudies #ProfessionalDevelopment #ResearchSkills

Causal Inference in R Course | Online Training | Statistical Horizons Live online course on causal inference in R using matching and weighting methods to estimate the causal effect of a treatment on an outcome.

Looking to strengthen your #causalinference skills? Join @noahgreifer.bsky.social on April 15-17 for "Causal Inference in R Using MatchIt and WeightIt" to gain the skills to apply these #Rstats packages to estimate and interpret treatment effects.

Rule 🥇: temporal leakage is a sin. You don't use the future to forecast the past.
Rule 🥈: don’t forecast what you can measure.
Rule 🥉: counterfactuals don’t get spoilers. If it knows what happened next, it's not a counterfactual. It’s fanfiction.
#forecasting #causalinference #mlsky

Key Topics in Causal Inference (KTCI). Dates: June 8-12, 2026. Taught by Miguel Hernán, Sara Lodi, Judith Lok, James Robins, Eric Tchetgen Tchetgen & Tyler VanderWeele

Want to build a foundation of #causalinference methodology?

Key Topics in Causal Inference (KTCI) is for researchers interested in acquiring a roadmap to the current causal research landscape.

📆 June 8-12, 2026
📍 In-person @hsph.harvard.edu / online

Learn more:
hsph.harvard.edu/research/cau...

The effect of SARS-CoV-2 testing on healthcare-seeking behaviour at primary care level AbstractBackground. Diagnostic self-testing for SARS-CoV-2 may lead to selection bias in test-negative case–control designs (TND) for COVID-19 vaccine effe

academic.oup.com/ije/article/...

#EpiSky #COVID19 #CausalSky #causalinference

07 - Beyond Confounders — Causal Inference for the Brave and True

#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...

Humans in the Loop: The Next Frontier in the Credibility Revolution Something is amiss in empirical economics. Despite the advances of the credibility revolution, published estimates tend to be inflated and overconfident. We arg

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...