# \#causalpy

**URL:** https://discourse.pymc.io/tag/causalpy/101.md

[Latest](https://discourse.pymc.io/latest.md) · [Categories](https://discourse.pymc.io/categories.md) · [Tags](https://discourse.pymc.io/tags.md)

---

## [CausalPy - Intuition behind calculation of causal impact and treatment time inputs](https://discourse.pymc.io/t/causalpy-intuition-behind-calculation-of-causal-impact-and-treatment-time-inputs/17069)

<div class="topic-metadata">

**Author:** [@ykarle](https://discourse.pymc.io/u/ykarle)\
**Replies:** 8\
**Last updated:** [July 29, 2025, 11:59am UTC](https://discourse.pymc.io/t/causalpy-intuition-behind-calculation-of-causal-impact-and-treatment-time-inputs/17069 "2025-07-29T11:59:21Z")

</div>

Was following the below CausalPy tutorial for Interrupted Time Series and using it for a simple pre-post comparison. Pre-world consists of the old world which had a old model controlling the outputs and post-world cons…

---

## [CausalPy correlation matrix interpretation](https://discourse.pymc.io/t/causalpy-correlation-matrix-interpretation/17138)

<div class="topic-metadata">

**Author:** [@ykarle](https://discourse.pymc.io/u/ykarle)\
**Replies:** 3\
**Last updated:** [July 7, 2025, 1:10pm UTC](https://discourse.pymc.io/t/causalpy-correlation-matrix-interpretation/17138 "2025-07-07T13:10:34Z")

</div>

Based on the Instrumental Variable Modelling (IV) with pymc models — CausalPy 0.4.2 documentation article, it is obvious that there is a positive correlation between the treatment variable (X: risk) and the outcome va…

---

## [Per subject mean centered X and y for fixed effects model](https://discourse.pymc.io/t/per-subject-mean-centered-x-and-y-for-fixed-effects-model/16484)

<div class="topic-metadata">

**Author:** [@spdrnl](https://discourse.pymc.io/u/spdrnl)\
**Replies:** 6\
**Last updated:** [February 4, 2025, 4:38pm UTC](https://discourse.pymc.io/t/per-subject-mean-centered-x-and-y-for-fixed-effects-model/16484 "2025-02-04T16:38:19Z")

</div>

Hi, My goal is to replicate a causal model from the book Effect, namely the fixed effects model. Does anyone have experience with this model? The model is a fixed effects model that simply regresses per subject mean ce…

---

## [CausalPy SyntheticControl With multiple cities in the Treatment](https://discourse.pymc.io/t/causalpy-syntheticcontrol-with-multiple-cities-in-the-treatment/14633)

<div class="topic-metadata">

**Author:** [@Joao\_Fidalgo](https://discourse.pymc.io/u/Joao_Fidalgo)\
**Replies:** 3\
**Last updated:** [June 18, 2024, 9:10pm UTC](https://discourse.pymc.io/t/causalpy-syntheticcontrol-with-multiple-cities-in-the-treatment/14633 "2024-06-18T21:10:39Z")

</div>

Hi, looking at causalPy example for geolift Bayesian geolift with CausalPy — CausalPy 0.3.0 documentation Is it possible to include multiple cities in the treatment? formula = “”" Denmark ~ 0 + Austria + Belgium + Bul…

---

## [CausalPy - Evaluating uncertainty for Interrupted time series](https://discourse.pymc.io/t/causalpy-evaluating-uncertainty-for-interrupted-time-series/13491)

<div class="topic-metadata">

**Author:** [@aabugaev](https://discourse.pymc.io/u/aabugaev)\
**Replies:** 9\
**Last updated:** [March 2, 2024, 11:47am UTC](https://discourse.pymc.io/t/causalpy-evaluating-uncertainty-for-interrupted-time-series/13491 "2024-03-02T11:47:27Z")

</div>

Hello, PyMC community! First of all, thanks a lot for CausalPy and including so many designs to it. I’ve got a couple of questions about Interrupted Time Series design and the Intervals it’s providing and would apprecia…