Add details about causal impact in the interrupted time series docs #504
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NathanielF
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Jul 18, 2025
| @@ -7,7 +7,45 @@ | |||
| "source": [ | |||
| "# Example Interrupted Time Series (ITS) with `pymc` models\n", | |||
| "\n", | |||
| "This notebook shows an example of using interrupted time series, where we do not have untreated control units of a similar nature to the treated unit and we just have a single time series of observations and the predictor variables are simply time and month." | |||
| "Interrupted Time Series (ITS) analysis is a powerful approach for estimating the causal impact of an intervention or treatment when you have a single time series of observations. The key idea is to compare what actually happened after the intervention to what would have happened in the absence of the intervention (the \"counterfactual\"). To do this, we train a statistical model on the pre-intervention data (when no treatment has occurred) and then use this model to forecast the expected outcomes into the post-intervention period. The difference between the observed outcomes and these model-based counterfactual predictions provides an estimate of the causal effect of the intervention, along with a measure of uncertainty if using a Bayesian approach.\n", | |||
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... outcomes into the post-intervention period [as if treatment hadn't occurred]. The difference
| "This notebook shows an example of using interrupted time series, where we do not have untreated control units of a similar nature to the treated unit and we just have a single time series of observations and the predictor variables are simply time and month." | ||
| "Interrupted Time Series (ITS) analysis is a powerful approach for estimating the causal impact of an intervention or treatment when you have a single time series of observations. The key idea is to compare what actually happened after the intervention to what would have happened in the absence of the intervention (the \"counterfactual\"). To do this, we train a statistical model on the pre-intervention data (when no treatment has occurred) and then use this model to forecast the expected outcomes into the post-intervention period. The difference between the observed outcomes and these model-based counterfactual predictions provides an estimate of the causal effect of the intervention, along with a measure of uncertainty if using a Bayesian approach.\n", | ||
| "\n", | ||
| "This notebook shows an example of using interrupted time series, where we do not have untreated control units of a similar nature to the treated unit and we just have a single time series of observations and the predictor variables are simply time and month. So the only real way to estimate the counterfactual is by training a model on the pre-intervention data and then using this model to forecast the expected outcomes into the post-intervention period." |
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"where we do not have untreated...." awkward construction too many negatives.
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This PR just expands on what we mean by "causal impact" in the interrupted time series setting.
📚 Documentation preview 📚: https://causalpy--504.org.readthedocs.build/en/504/