Mastodawn

https://pgmpy.org is a Python package for causal inference and probabilistic inference using Directed Acyclic Graphs (#DAGs) and Bayesian Networks with a focus on modularity and extensibility.
Written and maintained by Ankur Ankan and Johannes Textor.

Supported Data Types

pgmpy is a Python package for causal inference and probabilistic inference using Directed Acyclic Graphs (DAGs) and Bayesian Networks with a focus on modularity and extensibility. Implementations o...

pgmpy

Dr Mircea Zloteanu 🌺🌞🍃Feb 10

#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

https://pedermisager.org/blog/causion-dag-simulator/

Introducing Causion: A web app for playing with DAGs | Peder M. Isager

Personal website of Dr. Peder M. Isager

Peder M. Isager

Dr Mircea Zloteanu 🌺🌞🍃Jan 29

#statstab #474 {DeclareDesign} Observational : causal

Thoughts: If you want to do causal research with OS there is a lot more to consider than in #473

#design #research #rstats #education #tutorial #pedagogy #DiD #DAGs #causalinference

https://book.declaredesign.org/library/observational-causal.html

16 Observational : causal – Research Design in the Social Sciences

Mysse.m.m.Jan 28

It has been snowing again. Went on a walk in the park, feeding the squarrels with some nuts. I love snow! #snow #walkingthedog #østreanlæg #squarrels #nuts #dog #dags