🧪 Causal inference is about understanding why things happen, not just what
Alex Andorra talks with Sam Witty about ChiRho & how probabilistic programming is reshaping interventions, counterfactuals, and the future of causal reasoning
🎧https://learnbayesstats.com/episode/141-ai-assisted-causal-inference-sam-witty
#CausalInference #BayesianStatistics #Podcast #DataScience #AIResearch #LearningBayesianStatistics #NewEpisode
Postdoc in Single-Cell Multi-Omic Gene Regulatory Networks
University of Massachusetts Chan Medical School
Decode #GeneRegulatoryNetwork from #SingleCell multiomics with #CausalInference and #DiffEq as a #postdoc! No biomed bg needed.
See the full job description on jobRxiv: https://jobrxiv.org/job/university-of-massachusetts-chan-medical-...
https://jobrxiv.org/job/university-of-massachusetts-chan-medical-school-27778-postdoc-in-single-cell-multi-omic-gene-regulatory-networks-2/?fsp_sid=2615
P2: #causalinference #causality #inference #statistic #observability #llm #reasoning
| Data types | Requires model integration | Handles via prompting in one model |
```
**LLM Limitations:** LLMs use pattern matching over
explicit causal modeling.
- No explicit causal graphs/mechanisms—only patterns and
correlations.
- Lack modular separation, functions are entwined.
- Risk of hallucinated causal links, unreliable for
interventions.
- Formal counterfactuals need extensive external
scaffolding.
**Fields:**
- **Healthcare:** Predict treatment outcomes (reasoner),
explain intervention effects (explainer), recommend
actions (producer).
- **Economics/Policy:** Assess impacts, clarify causal
pathways, propose policies.
- **Recommendation Systems:** Infer preferences, explain
choices, personalize outputs.
Text of original post: https://try-codeberg.github.io/static/causal-inference.org
P2: P1: #causalinference #causality #inference #statistic #observability #llm #reasoning
needing rigorous, transparent causality.
- Effective for quick prototyping or low-risk tasks where
simulated causal logic suffices.
```text
| | **Causal Inference Neural Networks** | **Prompt-Engineered Multimodal LLM** |
|--------------+--------------------------------------+-------------------------------------------|
| Causality | Explicit, modeled, testable | Pattern-based, plausible but implicit |
| Reliability | High (given good data/model) | Medium, can produce errors/hallucinations |
| Transparency | Modular, explainable | Opaque, explanation quality varies |
| Scalability | Harder (custom per domain/signal) | Easier (generalizable across domains) |
P1: P1: #causalinference #causality #inference #statistic #observability #llm #reasoning
Topic: Causal LLM or splitting LLM
Causal Cooperative Networks (CCNets) - Causal Learnign
Framework - Reasoner, Explainer, Producer.
Causal inference finds causes by showing they covary with
effects, occur beforehand, and by ruling out
alternatives.
LLMs use pattern matching, not explicit causal models or
separate reasoning modules.
- Insufficient for regulated or high-stakes domains
This Thursday is MadPy's next meetup. Pierce Edmiston will be providing a crash course in Causal Inference. It'll be a new location for our group: Sequoya Branch Library, on the near west side of #MadisonWI. We'll also have free pizza and beverages 🍕 🥤 Should be a great event. Looking forward to seeing everyone!
https://madpy.com/meetups/2025/9/11/20250911-a-crash-course-in-causal-inference/
This event is free and open the public. Newcomers and beginners are welcome
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!
See the full job description on jobRxiv: https://jobrxiv.org/job/university-of-massachusetts-chan-medical-school-27778-postdoc-i...
https://jobrxiv.org/job/university-of-massachusetts-chan-medical-school-27778-postdoc-in-single-cell-multi-omic-gene-regulatory-networks-2/?fsp_sid=2069
Julia Roher on Mediation Analysis
"The central concern is that claims about mediation are causal claims. We claim that some cause X affects an outcome Y via some mediator M, X → M → Y. Without reference to causality, in purely statistical terms, mediation is indistinguishable from confounding (X ← M → Y, MacKinnon et al., 2010) and really just not substantively meaningful"
Mediation analysis has gotten a lot of flak, including classic titles such as “Yes, but what’s the mechanism? (Don’t expect an easy answer)” (Bullock et al., 2010), “What mediation analysis can (not) do” (Fiedler et al., 2011), “Indirect effect ex machina” (The 100% CI, 2019), “In psychology everyth
#statstab #412 Causal inference for N-of-1 trials
Thoughts: Only skimmed this, curious what the #causalinference ppl think of this.
The aim of personalized medicine is to tailor treatment decisions to individuals' characteristics. N-of-1 trials are within-person crossover trials that hold the promise of targeting individual-specific effects. While the idea behind N-of-1 trials might seem simple, analyzing and interpreting N-of-1 trials is not straightforward. Here we ground N-of-1 trials in a formal causal inference framework and formalize intuitive claims from the N-of-1 trials literature. We focus on causal inference from a single N-of-1 trial and define a conditional average treatment effect (CATE) that represents a target in this setting, which we call the U-CATE. We discuss assumptions sufficient for identification and estimation of the U-CATE under different causal models where the treatment schedule is assigned at baseline. A simple mean difference is an unbiased, asymptotically normal estimator of the U-CATE in simple settings. We also consider settings where carryover effects, trends over time, time-varying common causes of the outcome, and outcome-outcome effects are present. In these more complex settings, we show that a time-varying g-formula identifies the U-CATE under explicit assumptions. Finally, we analyze data from N-of-1 trials about acne symptoms and show how different assumptions about the data generating process can lead to different analytical strategies.
🩺 How can digital twins help us move from hospital rankings to truly patient-centered comparisons?
🔗 Harnessing the power of virtual (digital) twins: Graphical causal tools for understanding patient and hospital differences. Computational and Structural Biotechnology Journal, DOI: https://doi.org/10.1016/j.csbj.2025.08.017
📚 CSBJ Smart Hospital: https://www.csbj.org/smarthospital
#DigitalTwins #HealthcareInnovation #CausalInference #PatientCare #PrecisionMedicine #HealthTech #DataDrivenHealthcare
Pierre-Simon Laplace
jobRxiv
Anoncheg
Madison Python
Tom Stafford
Dr Mircea Zloteanu ☀️ 🌊🌴
CSBJ