Estimating causal effects using geo experiments


Randomized experiments symbolize the gold normal for figuring out the causal results of app or web site design choices on consumer conduct. We may be focused on evaluating, for instance, totally different subscription provides, totally different variations of phrases and situations, or totally different consumer interfaces. Relating to on-line adverts, there may be additionally a basic must estimate the return on funding. Observational information comparable to paid clicks, web site visits, or gross sales may be saved and analyzed simply. Nevertheless, it’s usually not doable to find out the incremental influence of promoting by merely observing such information throughout time. One method that Google has lengthy used to acquire causal estimates of the influence of promoting is geo experiments.

What does it take to estimate the influence of on-line publicity on consumer conduct? Contemplate, for instance, an A/B experiment, the place one or the opposite model (A or B) of an online web page is proven at random to a consumer. The evaluation may then proceed with evaluating the possibilities of clicking on a sure hyperlink on the web page proven. The model of the net web page that has a considerably larger estimated chance of click on (click-through fee, or CTR) could be deemed the simpler one.

Equally, we may take a look at the effectiveness of a search advert in comparison with displaying solely natural search outcomes. Every time a consumer varieties in a selected search question, the system makes a split-second determination of whether or not or to not present a selected advert subsequent to the natural outcomes. Click on-through charges can then be in comparison with decide the relative effectiveness of the presence of adverts.

Visitors experiments like those above can randomize queries, however they can’t randomize customers. The identical consumer could also be proven the advert at any time when they carry out the identical search the second time. Visitors experiments, due to this fact, do not permit us to find out the long run impact of the advert on the conduct of customers.

We may approximate customers by cookies. Nevertheless, one consumer might have a number of gadgets (desktop, laptop computer, pill, smartphone), every with its personal cookie house. Furthermore, cookies get deleted and regenerated regularly. Cookie churn will increase the possibilities {that a} consumer might find yourself receiving a combination of each the lively remedy and the management remedy.

Even when we have been capable of preserve a file of all searches and advert clicks and affiliate them with on-line conversions, we might nonetheless not be capable of observe any long-term conduct. A conversion would possibly occur days after the advert was seen, maybe at a daily brick-and-mortar retailer. This makes it tough to attribute the impact of an internet advert on offline purchases.

It is necessary that we will measure the impact of those offline conversions as nicely. How can we join an occasion of buy to the occasion of perceiving the advert if the acquisition doesn’t occur instantly? And, from the angle of the experiment set-up, how can we be certain that a consumer whom we assigned to the management group will not ever see the advert through the experiment?

One other risk is to run a panel research, an experiment with a recruited set of customers who permit us to research their internet and app utilization, and their buy conduct. Panel research make it doable to measure consumer conduct together with the publicity to adverts and different on-line parts. Nevertheless, significant insights require a consultant panel or an evaluation that corrects for the sampling bias which may be current. As well as, panel research are costly. Would not it’s nice if we did not require particular person information to estimate an combination impact? Let’s check out bigger teams of people whose combination conduct we will measure.

A geo experiment is an experiment the place the experimental items are outlined by geographic areas. Such areas are sometimes called Generalized Market Areas (GMAs) or just geos. They’re non-overlapping geo-targetable regions. This implies it’s doable to specify precisely through which geos an advert marketing campaign shall be served  and to look at the advert spend and the response metric on the geo stage. We can then type remedy and management teams by randomizing a set of geos.

Contemplate, for instance, the partition of america into 210 GMAs defined by Nielsen Media. The areas have been initially shaped primarily based on tv viewing conduct of their residents, clustering collectively “unique geographic space of counties through which the house market tv stations maintain a dominance of complete hours seen.” These geos may be focused individually in Google AdWords. Right here is an instance of a randomized task:

In distinction to the US, France would not at the moment have an identical set of geos. So we created a set of geos utilizing our personal clustering algorithms. The determine beneath reveals an instance of a set of geos partitioning mainland France into 29 GMAs:

Suppose that customers in management areas are served adverts with a complete spend depth of $C$ {dollars} per week, whereas customers in remedy areas are served adverts with a value of $T = C + A$ {dollars} per week ($A > 0$). The important thing assumption in geo experiments is that customers in every area contribute to gross sales solely of their respective area. This assumption permits us to estimate the impact of the advert spend on gross sales. What makes geo experiments so easy and highly effective is that they permit us to seize the total results of promoting, together with offline gross sales and conversions over longer durations of time (e.g., days or even weeks).

Measuring the effectiveness of on-line advert campaigns

Estimating the causal results of an promoting marketing campaign, and the worth of promoting typically, is what every of Google’s promoting purchasers wish to do for every of their merchandise. There are numerous strategies for estimating causal results, and but getting it proper stays a difficult downside in follow.

The amount that we goal to estimate, specifically, is a selected sort of return on funding the place the funding is the price of promoting. We regularly confer with this because the Return On Advert Spend (ROAS). Much more particularly, we’re sometimes within the change in gross sales (or web site visits, conversions, and many others.) once we change the advert spend: the incremental ROAS, or iROAS. When response is expressed when it comes to the identical foreign money because the funding, iROAS is only a scalar. For instance, an iROAS of three signifies that every additional greenback invested in promoting results in Three incremental {dollars} in income. Alternatively, when the response is the variety of conversions, iROAS may be given when it comes to variety of conversions per extra unit of foreign money invested, say “1000 incremental conversions per 10,000 extra {dollars} spent.” In different phrases, iROAS is the slope of a curve of the response metric plotted in opposition to the underlying promoting spend.

Construction of a geo experiment

A typical geo experiment consists of two distinct time durations: pretest and take a look at. Throughout the pretest interval, there are not any variations within the advert marketing campaign construction throughout geos. All geos function on the identical baseline stage; the incremental distinction between the management and remedy geos is zero in expectation. The pretest interval is usually Four to eight weeks lengthy.

Throughout the take a look at interval (which is usually Three to five weeks lengthy), geos within the remedy group are focused with modified promoting campaigns. We all know that this modification, by design, causes an incremental impact on advert spend. What we do not know is whether or not it additionally causes an incremental impact within the response metric. It’s value noting that changed campaigns might trigger the advert spend to extend (e.g., by including key phrases or growing bids within the AdWords public sale) or lower (e.g., by turning campaigns off). Both approach, we sometimes count on the response metric to be affected in the identical course as spend. In consequence, the iROAS is usually constructive.

After the take a look at interval finishes, the campaigns within the remedy group are reset to their unique configurations. This does not at all times imply their causal results will stop immediately. Incremental offline gross sales, for instance, could be delayed by days and even weeks. When learning delayed metrics, we might due to this fact wish to embody within the evaluation information from an extra cool-down interval to seize delayed results.

Designing a geo experiment: energy evaluation

As with all experiment, it’s important {that a} geo experiment is designed to have a excessive chance of being profitable. That is what we imply once we estimate the ability of the experiment: the chance of detecting an impact if an impact of a selected magnitude is actually current.

Statistical energy is historically given when it comes to a chance operate, however typically a extra intuitive approach of describing energy is by stating the anticipated precision of our estimates. We outline this as the usual error of the estimate instances the multiplier to acquire the bounds of a confidence interval. This might, for instance, be said because the “iROAS level estimate +/- 1.Zero for a 95% confidence interval”. It is a amount that’s simply interpretable and summarizes properly the statistical energy of the experiment.

The anticipated precision of our inferences may be computed by simulating doable experimental outcomes. We additionally test that the false constructive fee (i.e., the chance of acquiring a statistically vital end result if the true iROAS is actually zero) is appropriate, comparable to 5% or 10%. This energy evaluation is completely important within the design part as the quantity of proposed advert spend change straight contributes to the precision of the result. We will due to this fact decide whether or not a urged advert spend change is adequate for the experiment to be possible.

One of many components figuring out the usual error (and due to this fact, the precision) of our causal estimators is the quantity of noise within the response variable. The noisier the information, the upper the usual error. However, for the fashions that we use, the usual error of the iROAS estimate is inversely proportional to the advert spend distinction within the remedy group. That’s, we will purchase ourselves shorter confidence intervals (and a “larger precision”) by growing the advert spend distinction.

In follow, nevertheless, growing precision just isn’t at all times as simple as growing spend. There would possibly merely not be sufficient accessible stock (comparable to advert impressions, clicks, or YouTube views) to extend spend. Additional, there may be the chance that the elevated advert spend shall be much less productive resulting from diminishing returns (e.g., the primary 100 key phrases in a marketing campaign shall be extra environment friendly than the following 100 key phrases.) 

A mannequin for assessing incremental return on advert spend

We’re focused on estimating the iROAS, which for every geo is the ratio between the incremental (causal) income divided by the incremental change in expenditure in that geo. The incremental impact is outlined because the distinction between the anticipated potential consequence underneath remedy and the potential consequence underneath no remedy.

We estimate the causal remedy impact utilizing linear regression (see for instance [3], chapter 9). The mannequin regresses the outcomes $y_{1,i}$ on the incremental change in advert spend $delta_i$. We will nevertheless improve the precision of the estimates (and due to this fact the statistical energy) by additionally together with the pre-test response $y_{0,i}$ as a covariate:

$y_{1,i} = beta_0 + beta_1 y_{0,i} + beta_2 delta_i + epsilon_i$  for geo $i$

For all management geos, we have now $delta_i = 0$ by design. For a remedy geo $i$, $delta_i$ is the noticed advert spend minus the counterfactual, that’s, the advert spend that might have been noticed within the absence of the remedy. This counterfactual is estimated utilizing a linear regression mannequin utilized to regulate geos solely (see [1] for particulars). If there was no earlier advert expenditure (for this explicit channel), the counterfactual estimate would merely be zero, and so i might be the noticed spend in remedy geo $i$.

The modelled incremental response attributable to the change in advert spend $delta_i$ in geo $i$ is $beta_2 delta_i$. The mannequin parameter $beta_2$ is our important amount of curiosity, the incremental ROAS. For instance, $beta_2 = 3.1$ would point out that every unit of foreign money invested brought about an additional 3.1 items of foreign money generated.

The time period $beta_1 y_{0,i}$ controls for seasonality and different components widespread to all geos (e.g., a nationwide gross sales occasion), as it’s believable that the pretest and take a look at durations expertise a development or a short lived improve that’s unrelated to the impact we’re measuring. The interpretation of the coefficient $beta_1$, after all, is dependent upon the size of the pretest and take a look at durations.

The geos are invariably of various sizes and due to this fact the information present appreciable heteroscedasticity. Since every geo may be considered being constructed from small particular person contributions, we assume that the variance of a geo is proportional to its imply. It is a believable assumption, because the variance of a sum of unbiased variables is the same as the sum of their particular person variances. A variance stabilizing transformation (sq. root) is an choice right here to equalize the variances, however we choose to work on the unique scale (versus square-root items), as that is extra handy and versatile; for instance, the interpretation of the coefficients comparable to that of $beta_2$ (iROAS) is simple. We’ve got seen in follow that becoming the mannequin utilizing weighted regression with weights $1 / y_{0,i}$ (inverse of the sum of the response variable within the pretest interval) controls heteroskedasticity. With out weighted regression we might acquire a biased estimate of the variance. Warning is required, nevertheless, to make use of the weights: when the pre-test interval quantity of a geo are near zero, the weights could also be massive (this often displays a problem with information reporting). A fast treatment is to mix the smallest geos to type a brand new bigger geo.

The paper [1] offers extra particulars on the mannequin; the follow-up paper [2] describes an extension of the methodology to multi-period geo experiments.


Here’s a set of day by day time collection information of gross sales by geo. As we’ll see additional beneath, this era will type our pre-period for a geo experiment. There are 100 geos; the most important one has a quantity that’s 342 instances that of the smallest one. Such variations in scale aren’t uncommon. The time collection reveals vital weekly stage seasonality, with the bottom volumes occurring throughout weekends.

It’s often useful to have a look at these response variables on a log scale. This actually reveals how equally the geos behave, the one distinction being their measurement.

The aim of the research was to estimate the incremental return in gross sales on an extra advert spend change. The 100 geos have been randomly assigned to regulate and remedy teams, and a geo experiment take a look at interval was arrange for February 16  March 15, 2015, with the 6 earlier weeks serving because the pre-period. Throughout this take a look at interval, promoting spend was elevated within the remedy group. 

After the experiment was completed, the incremental return of advert spend was estimated to be $beta_2 = 3.1$. The opposite parameters have been $beta_0 = 74$, and $beta_1= 0.85$ with a residual normal deviation of 4.8. For the reason that pre-test interval is 6 weeks lengthy and the take a look at interval is Four weeks lengthy, a worth of $beta_1 = 0.85$ corresponds to a weekly development of $(6/4) instances 0.85 = 1.27$, that’s, on common every week the response volumes of the geos have a tendency to extend by 27% (within the absence of any intervention). In follow, the main target of the crew is nevertheless on the estimate of $beta_2$, to not neglect concerning the uncertainty round this estimate: the arrogance interval half-width was estimated to be 0.27.



As with all statistical mannequin, the mannequin is designed to function underneath sure assumptions. It is necessary that the technical assumptions of a linear regression mannequin are happy: linearity, additivity, independence of errors, normality, and equal variance of errors (after bearing in mind the weighting). If the response information certainly consists of sums of small unbiased contributions (say, gross sales of merchandise) which are grouped into geos, the variations between geos needs to be usually distributed. A extra vital assumption is that the relative quantity of the geos won’t be altering through the geo experiment.

It’s important to use applicable diagnostics to the information and to the mannequin match earlier than and after the take a look at. In follow, anomalies typically point out points with information reporting: untidy information and programming errors are a frequent supply for complications which are greatest addressed by creating instruments that confirm the integrity of the information and that test all mannequin assumptions. For instance, we have now developed just a few diagnostics instruments of our personal to catch surprising options within the information comparable to outliers.

The mannequin that we offered right here is sort of easy. It’s then value asking whether or not the mannequin accounts for all the components that would affect the response. As ordinary, the sincere reply is: in all probability not. One apparent omission is that the mannequin offered ignores different doable predictors comparable to pricing and promotion data. And such data might not even be accessible. Within the case of shopper packaged items comparable to gentle drinks or shampoo, for instance, even producers might not be conscious of all of the promotions getting in every of the shops promoting their merchandise. Because of randomization, this omission won’t bias our iROAS estimate. Nevertheless, it should sometimes improve its variance.

Forming remedy teams

By randomizing, we goal to generate the remedy and management teams with comparable baseline traits. By full randomization, we might nicely find yourself with teams that aren’t as balanced as we would favor. By default, we use stratified randomization with every stratum containing geos of comparable measurement. It might even be a good suggestion to contemplate forming strata by different traits comparable to geographical location.

Even stratified randomization might not be sufficient: the issue of steadiness could also be particularly accentuated when we have now few geos to work with (for instance, in small international locations). Extremely heterogeneous international locations typically have their very own set of challenges. For instance, the metropolitan areas of London and Paris dominate total international locations just like the UK or France. This makes it nearly not possible to partition such international locations into homogeneous, well-balanced teams.

For that reason, in these explicit conditions we want an algorithm that matches geos to type management and remedy teams which are predictive of one another, justifying a causal interpretation of the evaluation.


A specific nuisance is a geo that – for a identified or unknown motive – experiences a shock (surprising drop or surge) within the response variable through the take a look at. For instance, if a big geo within the management group out of the blue experiences a warmth wave, a geo experiment to measure the incremental gross sales of a selected ice cream model may be affected, diluting the impact that we’re aiming to measure. If the shock is balanced by one other geo within the different group, this downside is luckily mitigated (correctly in a randomized experiment). In any other case we must cope with the outlier geo(s) after the experiment. Therefore we want to pay attention to these issues and develop complementary methodologies to cope with them.

It’s a good suggestion to do a sensitivity evaluation to test if there may be any trigger for concern. For instance, one helpful diagnostic to detect a single outlier geo is to have a look at the impact of every geo on the iROAS level estimate. On this leave-one-out evaluation we repeat the evaluation as many instances as there are geos, every time dropping one geo from the evaluation. By a histogram of the estimates, we might even see that dropping a selected geo might have a transparent impact on the iROAS estimate.

Small market areas

For the reason that statistical energy of this mannequin is dependent upon the variety of geos, it’s not clearly appropriate for conditions through which the variety of geos could be very small. Within the U.S., for nationwide stage experiments we have now no downside as we have now over 210 geos. In Europe, use of this system might not be at all times advisable; as a tough rule of thumb, we choose to use this method to experiments with 30, or extra, geos. If this isn’t the case, different approaches counting on time-series fashions, comparable to CausalImpact, could also be a better option.

What’s subsequent?

We talked about a number of challenges: making certain the mannequin is acceptable, lack of helpful information, forming comparable remedy and management teams, dominant geos, outliers, and operating experiments in small international locations. For an information scientist, all this implies extra attention-grabbing and relevant analysis alternatives.


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