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You may have heard that most published research is false (Ionnadis). But what you probably don’t know is that most corporate data science is also false.

Gaurav Sood

The returns on data science in most companies are likely sharply negative. There are a few reasons for that. First, as with any new ‘hot’ field, the skill level of the average worker is low. Second, the skill level of the people managing these workers is also low—most struggle to pose good questions, and when they stumble on one, they struggle to answer it well. Third, data science often fails silently (or there is enough corporate noise around it that most failures are well-hidden in plain sight), so the opportunity to learn from mistakes is small. And if that was not enough, many companies reward speed over correctness, and in doing that, often obtain neither.

How can we improve on the status quo? The obvious remedy for the first two issues is to increase the skill by improving training or creating specializations. And one remedy for the latter two points is to create incentives for doing things correctly.

Increasing training and creating specializations in data science is expensive and slow. Vital, but slow. Creating the right incentives for good data science work is not trivial either. There are at least two large forces lined up against it: incompetent supervisors and the fluid and collaborative nature of work—work usually involves multiple people, and there is a fluid exchange of ideas. Only the first is fixable—the latter is a property of work. And fixing it comes down to making technical competence a much more important criterion for hiring.

Aside from hiring more competent workers or increasing the competence of workers, you can also simulate the effect by using checklists—increase quality by creating a few “pause points”—times during a process where the person (team) pauses and goes through a standard list of questions.

To give body to the boast, let me list some common sources of failures in DS and how checklists at different pause points may reduce failure.

1. Learn what you will lose in translation. Good data science begins with a good understanding of the problem you are trying to solve. Once you understand the problem, you need to translate it into a suitable statistical analog. During translation, you need to be aware that you will lose something in the translation.
2. Learn the limitations. Learn what data you would love to have to answer the question if money was no object. And use it to understand how far do you fall short from that ideal and then come to a judgment about whether the question can be answered reasonably with the data at hand.
3. Learn how good the data are. You may think you have the data, but it is best to verify it. For instance, it is good practice to think through the extent to which a variable captures the quantity of interest.
4. Learn the assumptions behind the formulas you use and test the assumptions to find the right thing to do. Thou shall only use math formulas when you know the limitations of such formulas. Having a good grasp of when formulas don’t work is essential. For instance, say the task is to describe a distribution. Someone may use the mean and standard deviation to describe it. But we know that these sufficient statistics vary by distribution. For binomial, it may just be p. A checklist for “describing” a variable can be:
   1. check skew by plotting: averages are useful when distributions are symmetric, and lots of observations are close to the mean. If skewed, you may want to describe various percentiles.
   2. how many missing values and what explains the missing values.
   3. check for unusual values and what explains the ‘unusual’ values.

The paper (pdf) makes the case that the primary reason for electoral cycles in dissents is priming. The paper notes three competing explanations: 1) caseload composition, 2) panel composition, and 3) volume of caseloads. And it “rules them out” by regressing case type, panel composition, and caseload on quarters from the election (see Appendix Table D). The coefficients are uniformly small and insignificant. But is that enough to rule out alternate explanations? No. Small coefficients don’t imply that there is no path from proximity to the election via competing mediators to dissent (if you were to use causal language). We can only conclude that the pathway doesn’t exist if there is a sharp null. The best you can do is bound the estimated effect.

Say that you want to predict wait times at restaurants using data with four columns: wait times (wait), the restaurant name (restaurant), time and date of observation. Using the time and date of the observation, you create two additional columns: time of the day (tod) and day of the week (dow). And say that you estimate the following model:

Assume that the number of rows is about 100 times the number of columns. There is little chance of overfitting. But you still do an 80/20 train/test split and pick the model that works the best OOS.

You have every right to expect the model’s performance to be close to its OOS performance. But when you deploy the model, the model performs much worse than that. What could be going on?

In the model, we estimate a restaurant level intercept. But in estimating the intercept, we use data from all wait times, including those that happened after the date. One fix is to using rolling averages or last X wait times in the regression. Another is to more formally construct the data in such a way that you are always predicting the next wait time.

Forward Stepwise Regression (FSR) is hardly used today. That is mostly because regularization is a better way to think about variable selection. But part of the reason for its disuse is that FSR is a greedy optimization strategy with unstable paths. Jigger the data a little and the search paths, variables in the final set, the performance of the final model, all can change dramatically. The same issues, however, affect another greedy optimization strategy—CART. The insight that rehabilitated CART was bagging—build multiple trees using random subspaces (sometimes on randomly sampled rows) and average the results. What works for CART should principally also work for FSR. If you are using FSR for prediction, you can build multiple FSR models using random subspaces and random samples of rows and then average the results. If you are using it for variable selection, you can pick variables with the highest batting average (n_selected/n_tried). (LASSO will beat it on speed but there is little reason to expect that it will beat it on results.)

Say that you want to persuade a group of people to go out and vote. You can reach people by phone, mail, f2f, or email. And the cost of reaching out f2f > phone > mail > email. Your objective is to convert as many people as possible. How would you do it?

Thompson sampling provides one answer. Thompson sampling “randomly allocates subjects to treatment arms according to their probability of returning the highest reward under a Bayesian posterior.”

To exploit it, start by predicting persuasion (or persuasion/$) based on whatever you know about the person, and assignment to treatment or control. Conventionally, this means using a random forest model to estimate heterogeneous treatment effects but really use whatever gets you the best fit after including interactions in the inputs. (Make sure you get calibrated probabilities back.) Use the forecasted probabilities to find the treatment arm with the highest reward and probabilistically assign people to that.

Here’s the fun part: the strategy also accounts for compliance. The kinds of people who don’t ‘comply’ with one method, e.g., don’t pick up the phone, will be likelier to be assigned to another method.

This is not a note about George Box’s quote about models. Neither is it about explainability. The first is trite. And the second is a mug’s game.

Imagine the following: you get hundreds of emails a day, and someone must manually sort which emails are urgent and which are not. The process is time-consuming. So you want to build a model. You estimate that a model with an error rate of 5% or less will save time—the additional work from addressing the erroneous five will be outweighed by the “free” correct classification of the other 95.

Say that you build a model. And if you dichotomize at p = .5, the model accurately classifies 70% of all emails. Even though the accuracy is less than 95%, should we put the model in production?

Often, the answer is yes. When you put such a model in production, it generally saves effort right away. Here’s how. If you get people to (continue to) manually classify the emails that the model is uncertain about, say with p-values between .3 and .7, the accuracy of the model on the rest of rows is generally vastly higher. More generally, you can choose the cut-offs for which humans need to code in a way that reduces the error to an acceptable level. And then use a hybrid approach to capitalize on the savings and like Matthew 22:21, render to model the region where the model does well, and to humans the rest.