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title: "CausalImpact"
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### An R package for causal inference using Bayesian structural time-series models

#### What does the package do?

This R package implements an approach to estimating the causal effect of a
designed intervention on a time series. For example, how many additional daily
clicks were generated by an advertising campaign? Answering a question like this
can be difficult when a randomized experiment is not available.

#### How does it work?

Given a response time series (e.g., clicks) and a set of control time series
(e.g., clicks in non-affected markets or clicks on other sites), the package
constructs a Bayesian structural time-series model. This model is then used to
try and predict the counterfactual, i.e., how the response metric would have
evolved after the intervention if the intervention had never occurred. For a
quick overview, watch the
[tutorial video](https://www.youtube.com/watch?v=GTgZfCltMm8).
For details, see:
[Brodersen et al., Annals of Applied Statistics (2015)](https://research.google/pubs/inferring-causal-impact-using-bayesian-structural-time-series-models/).

#### What assumptions does the model make?

As with all non-experimental approaches to causal inference, valid conclusions
require strong assumptions. In the case of CausalImpact, we assume that there is
a set control time series that were
*themselves not affected by the intervention.* If they were, we might falsely
under- or overestimate the true effect. Or we might falsely conclude that there
was an effect even though in reality there wasn't. The model also assumes that
the relationship between covariates and treated time series, as established
during the pre-period, remains stable throughout the post-period (see
`model.args$dynamic.regression` for a way of relaxing this assumption). Finally,
it's important to be aware of the *priors* that are part of the model (see
`model.args$prior.level.sd` in particular).

#### How is the package structured?

The package is designed to make counterfactual inference as easy as fitting a
regression model, but much more powerful, provided the assumptions above are
met. The package has a single entry point, the function `CausalImpact()`. Given
a response time series and a set of control time series, the function constructs
a time-series model, performs posterior inference on the counterfactual, and
returns a `CausalImpact` object. The results can be summarized in terms of a
table, a verbal description, or a plot.


## 1. Installing the package

`CausalImpact` is available on
[CRAN](https://cran.r-project.org/package=CausalImpact) and can be installed as
follows in an R session:

```{r, eval=FALSE}
install.packages("CausalImpact")
```

Once installed, the package can be loaded in a given R session using:

```{r, message=FALSE, warning=FALSE}
library(CausalImpact)
```

## 2. Creating an example dataset

To illustrate how the package works, we create a simple toy dataset. It consists
of a response variable `y` and a predictor `x1`. Note that in practice, we'd
strive for including many more predictor variables and let the model choose an
appropriate subset. The example data has 100 observations. We create an
*intervention effect* by lifting the response variable by 10 units after
timepoint 71.
```{r}
set.seed(1)
x1 <- 100 + arima.sim(model = list(ar = 0.999), n = 100)
y <- 1.2 * x1 + rnorm(100)
y[71:100] <- y[71:100] + 10
data <- cbind(y, x1)
```

We now have a simple matrix with 100 rows and two columns:
```{r}
dim(data)
head(data)
```

We can visualize the generated data using:
```{r, eval=FALSE}
matplot(data, type = "l")
```
```{r, echo=FALSE, fig.width=5, fig.height=2.8}
par(cex = 0.85, oma = c(0, 0, 0, 0), mar = c(3, 2, 1, 1))
matplot(data, type = "l", lwd = 1.5)
```

## 3. Running an analysis

To estimate a causal effect, we begin by specifying which period in the data
should be used for training the model (*pre-intervention period*) and which
period for computing a counterfactual prediction (*post-intervention period*).
```{r}
pre.period <- c(1, 70)
post.period <- c(71, 100)
```

This says that time points 1 ... 70 will be used for training, and time points
71 ... 100 will be used for computing predictions. Alternatively, we could
specify the periods in terms of dates or time points; see
[Section 5](#working-with-dates-and-times) for an example.

To perform inference, we run the analysis using:
```{r}
impact <- CausalImpact(data, pre.period, post.period)
```

This instructs the package to assemble a structural time-series model, perform
posterior inference, and compute estimates of the causal effect. The return
value is a `CausalImpact` object.

## 4. Plotting the results

The easiest way of visualizing the results is to use the `plot()` function that
is part of the package:
```{r, fig.width=8, fig.height=6, eval=FALSE}
plot(impact)
```
```{r, include=FALSE}
library(ggplot2)
```
```{r, echo=FALSE}
q <- plot(impact) + theme_bw(base_size = 11)
suppressWarnings(plot(q))
```

By default, the plot contains three panels. The first panel shows the data and a
counterfactual prediction for the post-treatment period. The second panel shows
the difference between observed data and counterfactual predictions. This is the
*pointwise* causal effect, as estimated by the model. The third panel adds up
the pointwise contributions from the second panel, resulting in a plot of the
*cumulative* effect of the intervention.

Remember, once again, that all of the above inferences depend critically on the
assumption that the covariates were not themselves affected by the intervention.
The model also assumes that the relationship between covariates and treated time
series, as established during the pre-period, remains stable throughout the
post-period.

## 5. Working with dates and times

It is often more natural to feed a time-series object into `CausalImpact()`
rather than a data frame. For example, we might create a `data` variable as
follows:

```{r}
time.points <- seq.Date(as.Date("2014-01-01"), by = 1, length.out = 100)
data <- zoo(cbind(y, x1), time.points)
head(data)
```

We can now specify the pre-period and the post-period in terms of time points
rather than indices:

```{r}
pre.period <- as.Date(c("2014-01-01", "2014-03-11"))
post.period <- as.Date(c("2014-03-12", "2014-04-10"))
```

As a result, the x-axis of the plot shows time points instead of indices:

```{r, fig.width=8, fig.height=6, eval=FALSE}
impact <- CausalImpact(data, pre.period, post.period)
plot(impact)
```
```{r, echo=FALSE}
impact <- CausalImpact(data, pre.period, post.period)
q <- plot(impact) + theme_bw(base_size = 11)
suppressWarnings(plot(q))
```

## 6. Printing a summary table

To obtain a numerical summary of the analysis, we use:
```{r}
summary(impact)
```

The **Average** column talks about the average (across time) during the
post-intervention period (in the example: time points 71 through 100). The
**Cumulative** column sums up individual time points, which is a useful
perspective if the response variable represents a flow quantity (such as
queries, clicks, visits, installs, sales, or revenue) rather than a stock
quantity (such as number of users or stock price).

In the example, the estimated average causal effect of treatment was 11 (rounded
to a whole number; for full precision see `impact$summary`). This is because we
observed an average value of 99 but would have expected an average value of only
89. The 95% posterior interval of the average effect is [9.8, 11]. Since this
excludes 0, we (correctly) conclude that the intervention had a causal effect on
the response variable. Since we generated the data ourselves, we know that we
injected a true effect of 10, and so the model accurately recovered ground
truth. One reason for this is that we ensured, by design, that the covariate
`x1` was not itself affected by the intervention. In practice, we must always
reason whether this assumption is justified.

For additional guidance about the correct interpretation of the summary table,
the package provides a verbal interpretation, which we can print using:
```{r, eval=FALSE}
summary(impact, "report")
```

The individual numbers in the table, at full precision, can be accessed using:
```{r, eval=FALSE}
impact$summary
```

See below for tips on how to use these commands with *knitr* / *R Markdown*.

## 7. Adjusting the model

So far, we've simply let the package decide how to construct a time-series model
for the available data. However, there are several options that allow us to gain
a little more control over this process. These options are passed into
`model.args` as individual list elements, for example:
```{r, eval=FALSE}
impact <- CausalImpact(..., model.args = list(niter = 5000, nseasons = 7))
```

### Available options

*   `niter` Number of MCMC samples to draw. More samples lead to more accurate
    inferences. Defaults to __1000__.

*   `standardize.data` Whether to standardize all columns of the data using
    moments estimated from the pre-intervention period before fitting the model.
    This is equivalent to an empirical Bayes approach to setting the priors. It
    ensures that results are invariant to linear transformations of the data.
    Defaults to __TRUE__.

*   `prior.level.sd` Prior standard deviation of the Gaussian random walk of the
    local level. Expressed in terms of data standard deviations. Defaults to
    __0.01__, a typical choice for  well-behaved and stable datasets with low
    residual volatility after regressing out known predictors (e.g., web
    searches or sales in high quantities). When in doubt, a safer option is to
    use __0.1__, as validated on synthetic data, although this may sometimes
    give rise to unrealistically wide prediction intervals.

*   `nseasons` Period of the seasonal components. In order to include a seasonal
    component, set this to a whole number greater than 1. For example, if the
    data represent daily observations, use 7 for a day-of-week component. This
    interface currently only supports up to one seasonal component. To specify
    multiple seasonal components, use bsts to specify the model directly, then
    pass the fitted model in as bsts.model. Defaults to __1__, which means no
    seasonal component is used.

*   `season.duration` Duration of each season, i.e., number of data points each
    season spans. Defaults to __1__. For example, to add a day-of-week component
    to data with daily granularity, use
    `model.args = list(nseasons = 7, season.duration = 1)`. To add a day-of-week
    component to data with hourly granularity, set
    `model.args = list(nseasons = 7, season.duration = 24)`.

*   `dynamic.regression` Whether to include time-varying regression
    coefficients. In combination with a time-varying local trend or even a
    time-varying local level, this often leads to overspecification, in which
    case a static regression is safer. Defaults to __FALSE__.

## 8. Using a custom model

Instead of using the default model constructed by the CausalImpact package, we
can use the bsts package to specify our own model. This provides the greatest
degree of flexibility.

Before constructing a custom model, we set the observed data in the
post-treatment period to NA, reflecting the fact that the counterfactual
response is unobserved after the intervention. We keep a copy of the actual
observed response in the variable `post.period.response`.
```{r}
post.period <- c(71, 100)
post.period.response <- y[post.period[1] : post.period[2]]
y[post.period[1] : post.period[2]] <- NA
```

We next set up and estimate a time-series model using the bsts package. Here is
a simple example:
```{r, echo=FALSE}
ss <- AddLocalLevel(list(), y)
bsts.model <- bsts(y ~ x1, ss, niter = 1000, ping = 0)
```
```{r, eval=FALSE}
ss <- AddLocalLevel(list(), y)
bsts.model <- bsts(y ~ x1, ss, niter = 1000)
```

Finally, we call `CausalImpact()`. Instead of providing input data, we simply
pass in the fitted model object (`bsts.model`). We also need to provide the
actual observed response. This is needed so that the package can compute the
difference between predicted response (stored in `bsts.model`) and actual
observed response (stored in `post.period.response`).
```{r}
impact <- CausalImpact(bsts.model = bsts.model,
                       post.period.response = post.period.response)
```

The results can be inspected in the usual way:
```{r, eval=FALSE}
plot(impact)
summary(impact)
summary(impact, "report")
```

## 9. FAQ

### How do I cite the package in my work?

We recommend referencing the use of the CausalImpact R package as shown in the
example below:

"CausalImpact `r packageVersion("CausalImpact")`, Brodersen et al., Annals of
Applied Statistics (2015). https://google.github.io/CausalImpact/"

To find out which package version you are using, type
`packageVersion("CausalImpact")`. See the bottom of this page for full
bibliographic details.

### How can I check whether the model assumptions are fulfilled?

It's the elephant in the room with any causal analysis on observational data:
how can we verify the assumptions that go into the model? Here are a few ways of
getting started. First of all, it is critical to reason why the covariates that
are included in the model (this was *x1* in the example)
*were not themselves affected* by the intervention. Sometimes it helps to plot
all covariates and do a visual sanity check. Next, it is a good idea to examine
how well the outcome data *y* can be predicted
*before the beginning of the intervention*. This can be done by running
`CausalImpact()` on an imaginary intervention. Then check how well the model
predicted the data following this imaginary intervention. We would expect not to
find a significant effect, i.e., counterfactual estimates and actual data should
agree reasonably closely. Finally, when presenting or writing up results, be
sure to list the above assumptions explicitly, including the priors in
`model.args`, and discuss them with your audience.

### May the data contain missing values?

The response variable (i.e., the first column in data) may contain missing
values (`NA`), but covariates (all other columns in data) may not. If one of
your covariates contains missing values, consider imputing (i.e., estimating)
the missing values; if this is not feasible, leave the regressor out.

### How can I customize the default plot?

By default, `plot()` creates three panels, showing the counterfactual,
pointwise, and cumulative impact estimates. One way of customizing the plot is
to specify which panels should be included:

```{r, eval=FALSE}
plot(impact, c("original", "pointwise"))
```

This creates a plot without cumulative impact estimates. This is sensible
whenever the response variable represents a stock quantity that cannot be
meaningfully summed up across time (e.g., number of current subscribers), rather
than a flow quantity (e.g., number of clicks).

### How can I change the font size in the plot?
The `plot()` function for `CausalImpact` objects returns a ggplot2 object. This
means we can customize the plot using standard ggplot2 functions. For example,
to increase the font size, we can do:

```{r, eval=FALSE}
library(ggplot2)
impact.plot <- plot(impact) + theme_bw(base_size = 20)
plot(impact.plot)
```

### How can I obtain 90% intervals instead of 95% intervals?
The size of the intervals is specified by the argument `alpha`, which defaults
to 0.05. To obtain 90% intervals instead, we would use:

```{r, eval=FALSE}
impact <- CausalImpact(data, pre.period, post.period, alpha = 0.1)
```

### Which predictor variables were used in the model?
Analyses may easily contain tens or hundreds of potential predictors (i.e.,
columns in the data function argument). Which of these were informative? We can
plot the posterior probability of each predictor being included in the model
using:

```{r, eval=FALSE}
plot(impact$model$bsts.model, "coefficients")
```
The size of the bars in the plot depict the inclusion probabilities of the
model coefficients. The bars are shaded by the conditional probability that a
coefficient is positive, given that it is nonzero (white means the coefficient
is negative, black means the coefficient is positive, grey means the coefficient
has the same probability of being negative or positive). For more information on
the plot, see
```{r, eval=FALSE}
?BoomSpikeSlab::PlotMarginalInclusionProbabilities
```

## 10. Further resources

### Literature

Brodersen KH, Gallusser F, Koehler J, Remy N, Scott SL.
Inferring causal impact using Bayesian structural time-series models.
*Annals of Applied Statistics*, 2015, Vol. 9, No. 1, 247-274.
https://research.google/pubs/inferring-causal-impact-using-bayesian-structural-time-series-models/

### Discussion forum

https://stats.stackexchange.com/


- - -

#### CausalImpact

An R package for causal inference using Bayesian structural time-series models

```{r, include=FALSE}
# Get authors and copyright information.
description <- packageDescription("CausalImpact")
authors.markdown <- paste(format(as.person(description$Author),
                                 include = c("given", "family")),
                          sep = ", ")
copyright.markdown <- gsub("\\(C\\)", "\u00a9", description$Copyright)
```

Version `r packageVersion("CausalImpact")`  
Licensed under the Apache License, Version 2.0.  
Authors: `r authors.markdown`  
`r copyright.markdown`
