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.

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. For details, see: Brodersen et al., Annals of Applied Statistics (2015).

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).

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.

`CausalImpact`

is available on
CRAN and can be installed as
follows in an R session:

```
install.packages("CausalImpact")
```

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

```
library(CausalImpact)
```

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.

```
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:

```
dim(data)
```

```
## [1] 100 2
```

```
head(data)
```

```
## y x1
## [1,] 105.2950 88.21513
## [2,] 105.8943 88.48415
## [3,] 106.6209 87.87684
## [4,] 106.1572 86.77954
## [5,] 101.2812 84.62243
## [6,] 101.4484 84.60650
```

We can visualize the generated data using:

```
matplot(data, type = "l")
```

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*).

```
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 for an example.

To perform inference, we run the analysis using:

```
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.

The easiest way of visualizing the results is to use the `plot()`

function that
is part of the package:

```
plot(impact)
```

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.

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:

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

```
## y x1
## 2014-01-01 105.2950 88.21513
## 2014-01-02 105.8943 88.48415
## 2014-01-03 106.6209 87.87684
## 2014-01-04 106.1572 86.77954
## 2014-01-05 101.2812 84.62243
## 2014-01-06 101.4484 84.60650
```

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

```
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:

```
impact <- CausalImpact(data, pre.period, post.period)
plot(impact)
```

To obtain a numerical summary of the analysis, we use:

```
summary(impact)
```

```
## Posterior inference {CausalImpact}
##
## Average Cumulative
## Actual 117 3511
## Prediction (s.d.) 107 (0.35) 3196 (10.60)
## 95% CI [106, 107] [3175, 3217]
##
## Absolute effect (s.d.) 11 (0.35) 316 (10.60)
## 95% CI [9.8, 11] [294.0, 337]
##
## Relative effect (s.d.) 9.9% (0.33%) 9.9% (0.33%)
## 95% CI [9.2%, 11%] [9.2%, 11%]
##
## Posterior tail-area probability p: 0.00101
## Posterior prob. of a causal effect: 99.8994%
##
## For more details, type: summary(impact, "report")
```

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:

```
summary(impact, "report")
```

The individual numbers in the table, at full precision, can be accessed using:

```
impact$summary
```

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

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:

```
impact <- CausalImpact(..., model.args = list(niter = 5000, nseasons = 7))
```

`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**.

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`

.

```
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:

```
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`

).

```
impact <- CausalImpact(bsts.model = bsts.model,
post.period.response = post.period.response)
```

The results can be inspected in the usual way:

```
plot(impact)
summary(impact)
summary(impact, "report")
```

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

“CausalImpact 1.2.5, 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.

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.

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.

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:

```
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).

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:

```
library(ggplot2)
impact.plot <- plot(impact) + theme_bw(base_size = 20)
plot(impact.plot)
```

The size of the intervals is specified by the argument `alpha`

, which defaults
to 0.05. To obtain 90% intervals instead, we would use:

```
impact <- CausalImpact(data, pre.period, post.period, alpha = 0.1)
```

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:

```
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

```
?BoomSpikeSlab::PlotMarginalInclusionProbabilities
```

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/pub41854/

https://stats.stackexchange.com/

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

Version 1.2.5

Licensed under the Apache License, Version 2.0.

Authors: Kay H. Brodersen, Alain Hauser

Copyright © 2014-2020 Google, Inc.