# Interrupted Time Series Analysis

Sometimes we want to know how an outcome variable for a single unit changed after an event or intervention. For example, if regulators announce sanctions against company A, we might want to know how the price of company A's stock changed after the announcement. Since we do not know what the price of Company A's stock would have been if the santions were not announced, we need some way to predict those values. An interrupted time series analysis does this by using some covariates that are related to the outcome but not related to whether the event happened to predict what would have happened. The estimated effects are the differences between the predicted post-event counterfactual outcomes and the observed post-event outcomes, which can also be aggregated to mean or cumulative effects. Estimating an interrupted time series design in CausalELM consists of three steps.

For a general overview of interrupted time series estimation see:

```
Bernal, James Lopez, Steven Cummins, and Antonio Gasparrini. "Interrupted time series
regression for the evaluation of public health interventions: a tutorial." International
journal of epidemiology 46, no. 1 (2017): 348-355.
```

The flavor of interrupted time series implemented here is similar to the variant proposed in:

```
Brodersen, Kay H., Fabian Gallusser, Jim Koehler, Nicolas Remy, and Steven L. Scott.
"Inferring causal impact using Bayesian structural time-series models." (2015): 247-274.
```

in that, although it is not Bayesian, it uses a nonparametric model of the pre-treatment period and uses that model to forecast the counterfactual in the post-treatment period, as opposed to the commonly used segment linear regression.

## Step 1: Initialize an interrupted time series estimator

The InterruptedTimeSeries constructor takes at least four agruments: pre-event covariates, pre-event outcomes, post-event covariates, and post-event outcomes, all of which can be either an array or any data structure that implements the Tables.jl interface (e.g. DataFrames). The interrupted time series estimator assumes outcomes are either continuous, count, or time to event variables.

Non-binary categorical outcomes are treated as continuous.

You can also specify which activation function to use, the number of extreme learning machines to use, the number of features to consider for each extreme learning machine, the number of bootstrapped observations to include in each extreme learning machine, and the number of neurons to use during estimation. These options are specified with the following keyword arguments: activation, num*machines, num*feats, sample*size, and num\*neurons.

```
# Generate some data to use
X₀, Y₀, X₁, Y₁ = rand(1000, 5), rand(1000), rand(100, 5), rand(100)
# We could also use DataFrames or any other package that implements the Tables.jl interface
# using DataFrames
# X₀ = DataFrame(x1=rand(1000), x2=rand(1000), x3=rand(1000), x4=rand(1000), x5=rand(1000))
# X₁ = DataFrame(x1=rand(1000), x2=rand(1000), x3=rand(1000), x4=rand(1000), x5=rand(1000))
# Y₀, Y₁ = DataFrame(y=rand(1000)), DataFrame(y=rand(1000))
its = InterruptedTimeSeries(X₀, Y₀, X₁, Y₁)
```

## Step 2: Estimate the Treatment Effect

Estimating the treatment effect only requires one argument: an InterruptedTimeSeries struct.

`estimate_causal_effect!(its)`

## Step 3: Get a Summary

We can get a summary of the model by pasing the model to the summarize method.

!!!note To calculate the p-value and standard error for the treatmetn effect, you can set the inference argument to false. However, p-values and standard errors are calculated via randomization inference, which will take a long time. But can be sped up by launching Julia with a higher number of threads.

`summarize(its)`

## Step 4: Validate the Model

For an interrupted time series design to work well we need to be able to get an unbiased prediction of the counterfactual outcomes. If the event or intervention effected the covariates we are using to predict the counterfactual outcomes, then we will not be able to get unbiased predictions. We can verify this by conducting a Chow Test on the covariates. An ITS design also assumes that any observed effect is due to the hypothesized intervention, rather than any simultaneous interventions, anticipation of the intervention, or any intervention that ocurred after the hypothesized intervention. We can use a Wald supremum test to see if the hypothesized intervention ocurred where there is the largest structural break in the outcome or if there was a larger, statistically significant break in the outcome that could confound an ITS analysis. The covariates in an ITS analysis should be good predictors of the outcome. If this is the case, then adding irrelevant predictors should not have much of a change on the results of the analysis. We can conduct all these tests in one line of code.

One can also specify the number of simulated confounders to generate to test the sensitivity of the model to confounding and the minimum and maximum proportion of data to use in the Wald supremum test by including the n, low, and high keyword arguments.

Obtaining correct estimates is dependent on meeting the assumptions for interrupted time series estimation. If the assumptions are not met then any estimates may be biased and lead to incorrect conclusions.

For a review of interrupted time series identifying assumptions and robustness checks, see:

```
Baicker, Katherine, and Theodore Svoronos. Testing the validity of the single
interrupted time series design. No. w26080. National Bureau of Economic Research, 2019.
```

`validate(its)`