We’re familiar with A/B tests that tell us how our metric (usually an average of some kind) changed due to the treatment. But if we want to get a better than average insight into the treatment effect, we should look beyond the mean. This post demonstrates why and how we might look at the way the quantiles of the distribution changed as a result of the treatment, complete with neat visualizations you can show in your next A/B test report built in Python.
Most companies I know of that include A/B testing in their product development process usually do something like the following for most of their tests:
This process is so common because, well, it works - if followed, it will usually result in the introduction of product features which increase our favorite metric. It creates a series of discrete steps in the product space which attempt to optimize the favorite metric without incurring unacceptable losses on the other metrics.
In this process, the average treatment effect is the star of the show. But as we learn in Stats 101, two distributions can look drastically different while still having the same average. For example, here are four remarkably different distributions with the same average:
from matplotlib import pyplot as plt import seaborn as sns import numpy as np from scipy.stats import poisson, skellam, nbinom, randint, geom for dist in [poisson(100), skellam(1101, 1000), randint(0, 200), geom(1./100)]: plt.plot(np.arange(0, 400), dist.pmf(np.arange(0, 400))) plt.xlim(0, 400) plt.ylabel('PMF') plt.title('Four distributions with a mean of 100') plt.show()
Similarly, the average treatment effect does not tell us much about how our treatment changed the shape of the distribution of outcomes. But we can expand our thinking not just to consider how the treatment changed the average, but the effect on the shape of the distribution; the distributional effect of the treatment. Expanding our thought to think about distributional effects might give us insights that we can’t get from averages alone, and help us see more clearly what our treatment did. For example:
The usual average treatment effect cannot answer these questions. We could compare single digit summaries of shape (variance, skewness, kurtosis) between treatment and control. However, even these are only simplified summaries; they describe a single attribute of the shape like the dispersion, symmetry, or heavy tailedness.
Instead, we’ll look at the empirical quantile function of control and treatment, and the difference between them. We’ll lay out some basic definitions here:
Let’s take a look at an example of how we might use these in practice to learn about the distributional effects of a test.
Let’s once more put ourselves in the shoes of that most beloved of Capitalist Heroes, the purveyor of little tiny cat sunglasses. Having harnessed the illuminating insights of your business’ data, you’ve consistently been improving your key metric of Revenue per Cat. You currently send out a weekly email about the current purrmotional sales, a newsletter beloved by dashing calicos and tabbies the world over. As you are the sort of practical, industrious person who is willing to spend their valuable time reading a blog about statistics, you originally gave this email the very efficient subject line of “Weekly Newsletter” and move on to other things.
However, you’re realizing it’s time to revisit that decision - your previous analysis demonstrated that warm eather is correlated with stronger sales, as cats everywhere flock to sunny patches of light on the rug in the living room. Perhaps, if you could write a suitably eye-catching subject line, you could make the most of this seasonal oppourtunity. Cats are notoriously aloof, so you settle on the overstuffed subject line “Wow so chic ✨ shades 🕶 for cats 😻 summer SALE ☀ buy now” in a desperate bid for their attention. As you are (likely) a person and not a cat, you decide to run an A/B test on this subject line to see if your audience likes the new subject line.
You fire up your A/B testing platform, and get 1000 lucky cats to try the new subject line, and 1000 to try the old one. You measure the revenue purr customer in the period after the test, and you’re ready to analyze the test results.
Lets import some things from the usual suspects:
from scipy.stats import norm, sem # Normal distribution, Standard error of the mean from copy import deepcopy import pandas as pd from tqdm import tqdm # A nice little progress bar from scipy.stats.mstats import mjci # Calculates the standard error of the quantiles: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.mstats.mquantiles_cimj.html from matplotlib import pyplot as plt # Pretty pictures import seaborn as sns # Matplotlib's best friend import numpy as np
In order to get a feel for how revenue differed between treatment and control, let’s start with our usual first tool for understanding distribution shape, the trusty histogram:
plt.title('Distribution of revenue per customer') sns.distplot(data_control, label='Control') sns.distplot(data_treatment, label='Treatment') plt.ylabel('Density') plt.xlabel('Revenue ($)') plt.legend() plt.show()
Hm. That’s a little tough to read. Just eyeballing it, the tail on the Treatment group seems a little thicker, but it’s hard to say much more than that.
Let’s see what we can learn about how treatment differs from control. We’ll compute the usual estimate of the average treatment effect on revenue per customer, along with its standard error.
def z_a_over_2(alpha): return norm(0, 1).ppf(1.-alpha/2.) te = np.mean(data_treatment) - np.mean(data_control) # Point estimate of the treatment effect ci_radius = z_a_over_2(.05) * np.sqrt(sem(data_treatment)**2 + sem(data_control)**2) # Propagate the standard errors of each mean, and compute a CI print('Average treatment effect: ', te, '+-', ci_radius)
Average treatment effect: 1.1241231969779277 +- 0.29768161367254564
Okay, so it looks like our treatment moved the average revenue per user! That’s good news - it means your carefully chosen subject line will actually translate into better outcomes, all for the low price of a changed subject line.
(An aside: in a test like this, you might pause here to consider other factors. For example: is there evidence that this is a novelty effect, rather than a durable change in the metric? Did I wait long enough to collect my data, to capture downstream events after the email was opened? These are good questions, but we will table them for now.)
It’s certainly good news that the average revenue moved. But, wise statistics sage that you are, you know the average isn’t the whole story. Now, lets think distributionally - let’s consider questions like:
We answer these questions by looking at how the distribution shifted.
(Another aside: For this particular problem related to the effects of an email change, we might also look at whether the treatment increased the open rate, or the average order value, or if they went in different directions. This is a useful way to decompose the revenue per customer, but we’ll avoid it in this discussion since it’s pretty email-specific.)
Before we talk about the quantile function, we can also consider another commonly used tool for inspecting distribution shape, which goes by the thematically-appropriate name of box-and-whisker plot.
Q = np.linspace(0.05, .95, 20) plt.boxplot(data_control, positions=, whis=[0, 100]) plt.boxplot(data_treatment, positions=, whis=[0, 100]) plt.xticks([0, 1], ['Control', 'Treatment']) plt.ylabel('Revenue ($)') plt.title('Box and Whisker - Revenue per customer by Treatment status') plt.show()
This isn’t especially easy to read either. We can get a couple of things from it: it looks like the max revenue per user in the treatment group was much higher, and the median was lower. (I also tried this one on a log axis, and didn’t find it much easier, but you may find that a more intuitive plot than I did.)
Let’s try a different approach to understanding the distribution shape - we’ll plot the empirical quantile function. We can get this using the
np.quantile function, and telling it which quantiles of the data we want to calculate.
plt.title('Quantiles of revenue per customer') plt.xlabel('Quantile') plt.ylabel('Revenue ($)') control_quantiles = np.quantile(data_control, Q) treatment_quantiles = np.quantile(data_treatment, Q) plt.plot(Q, control_quantiles, label='Control') plt.plot(Q, treatment_quantiles, label='Treatment') plt.legend() plt.show()
I find this a little easier to understand. Here are some things we can read off from it:
This is a much more detailed survey of the how the treatment affected our outcome than the average treatment effect can provide. At this point, we might decide to dive a little deeper into what happened with that 75% of users. If we can understand why they were affected negatively by the treatment, perhaps there is something we can do in the next iteration of the test to improve their experience.
Let’s look at this one more way - we’ll look at the treatment effect on the whole quantile curve. That is, we’ll subtract the control curve from the treatment curve, showing us how the treatment changed the shape of the distribution.
plt.title('Quantile difference (Treatment - Control)') plt.xlabel('Quantile') plt.ylabel('Treatment - Control') quantile_diff = treatment_quantiles - control_quantiles control_se = mjci(data_control, Q) treatment_se = mjci(data_treatment, Q) diff_se = np.sqrt(control_se**2 + treatment_se**2) diff_lower = quantile_diff - z_a_over_2(.05 / len(Q)) * diff_se diff_upper = quantile_diff + z_a_over_2(.05 / len(Q)) * diff_se plt.plot(Q, quantile_diff, color='orange') plt.fill_between(Q, diff_lower, diff_upper, alpha=.5) plt.axhline(0, linestyle='dashed', color='grey', alpha=.5) plt.show()
This one includes confidence intervals computed using the Maritz-Jarrett estimator of the quantile standard error. We’ve applied a Bonferroni correction to the estimates as well, so no one accuse us of a poor Familywise Error Rate.
We can read off from this chart where the statistically significant treatment effects on the quantile function are. Namely, the treatment lifted the top 25% of the revenue distribution, and depressed roughly the middle 50%. The mid-revenue users were less interested in the new subject line, but the fat cats in the top 25% of the distribution got even fatter; the entire treatment effect came from high-revenue feline fashionistas buying up all the inventory, so much so that it overshadowed the decrease in the middle.
The above analysis tells us more than the usual “average” analysis does; it lets us answer questions about how the treatment affects properties of the revenue distribution other than the mean. In a sense, we decomposed the average treatment effect by user quantile. But it’s not the only tool that lets us see how aspects of the distribution changed. There are some other methods we might consider as well:
Embarassingly, I have not yet achieved the level of free-market enlightment required to run a company that makes money by selling sunglasses to cats. Because of this fact, the data from this example was not actually collected by me, but generated by the following process:
sample_size = 1000 data_control = np.random.normal(0, 1, sample_size)**2 data_treatment = np.concatenate([np.random.normal(0, 0.01, round(sample_size/2)), np.random.normal(0, 2, round(sample_size/2))])**2