## Experiments Without Control

4 Jan

Say that you are in the search engine business. And say that you have built a model that estimates how relevant an ad is based on the ‘context’: search query, previous few queries, kind of device, location, and such. Now let’s assume that for context X, the rank-ordered list of ads based on expected profit is: product A, product B, and product C. Now say that you want to estimate how effective an ad for product A is in driving the sales of product A. One conventional way to estimate this is to randomly assign during serve time: for context X, serve half the people an ad for product A and serve half the people no ad. But if it is true (and you can verify this) that an ad for product B doesn’t cause people to buy product A, then you can switch the ‘no ad’ control where you are not making any money with an ad for product B. With this, you can estimate the effectiveness of ad for product A while sacrificing the least amount of revenue. Better yet, if it is true that ad for product A doesn’t cause people to buy product B, you can also at the same time get an estimate of the efficacy of ad for product B.

## The Benefit of Targeting

16 Dec

What is the benefit of targeting? Why (and when) do we need experiments to estimate the benefits of targeting? And what is the right baseline to compare against?

I start with a business casual explanation, using examples to illustrate some of the issues at hand. Later in the note, I present a formal explanation to precisely describe the assumptions to clarify under what conditions targeting may be a reasonable thing to do.

Say that you have some TVs to sell. And say that you could show an ad about the TVs to everyone in the city for free. Your goal is to sell as many TVs as possible. Does it make sense for you to build a model to pick out people who would be especially likely to buy the TV and only show an ad to them? No, it doesn’t. Unless ads make people less likely to purchase TVs, you are always better-off reaching out to everyone.

You are wise. You use common sense to sell more TVs than the guy who spent a bunch of money building the model and selling less. You make tons of money. And you use the money to buy Honda and Mercedes dealerships. You still retain the magical power of being able to show ads to everyone for free. Your goal is to maximize profits. And selling Mercedes nets you more profit than Hondas. Should you use a model to show some people ads about Toyota and other people ads about Honda? The answer is still no. Under likely to hold assumptions, the optimal strategy is to show an ad for Mercedes first and then an ad for Toyota. (You can show the Toyota ad first if people who want to buy Mercedes won’t buy a cheaper car if they see an ad for a cheaper car first.)

But what if you are limited to only one ad? What would you do? In that case, a model may make sense. Let’s see how things may look with some fake data. Let’s compare the outcomes of four strategies: two model-based targeting strategies and two target-everyone with one ad strategies. To make things easier, let’s assume that selling Mercedes nets ten units of profits and selling Honda nets five units of profit. Let’s also assume that people will only buy something if they see an ad for their preferred product.

## The Value of Predicting Bad Things

30 Oct

Foreknowledge of bad things is useful because it gives us an opportunity to a. prevent it, and b. plan for it.

Let’s refine our intuitions with a couple of concrete examples.

Many companies work super hard to predict customer ‘churn’—which customer is not going to use a product over a specific period (which can be the entire lifetime). If you know who is going to churn in advance, you can: a. work to prevent it, b. make better investment decisions based on expected cash flow, and c. make better resource allocation decisions.

Users “churn” because they don’t think the product is worth the price, which may be because a) they haven’t figured out a way to use the product optimally, b) a better product has come on the horizon, or c) their circumstances have changed. You can deal with this by sweetening the deal. You can prevent users from abandoning your product by offering them discounts. (It is useful to experiment to learn about the precise demand elasticity at various predicted levels of churn.) You can also give discounts is the form of offering some premium features free. Among people who don’t use the product much, you can run campaigns to help people use the product more effectively.

If you can predict cash-flow, you can optimally trade-off risk so that you always have cash at hand to pay your obligations. Churn can also help you with resource allocation. It can mean that you need to temporarily hire more customer success managers. Or it can mean that you need to lay off some people.

The second example is from patient care. If you could predict reasonably that someone will be seriously sick in a year’s time (and you can in many cases), you can use it to prioritize patient care, and again plan investment (if you were an insurance company) and resources (if you were a health services company).

Lastly, as is obvious, the earlier you can learn, the better you can plan. But generally, you need to trade-off between noise in prediction and headstart—things further away are harder to predict. The noise-headstart trade-off is something that should be done thoughtfully and amended based on data.

## Targeting 101

22 Jun

Targeting Economics

Say that there is a company that makes more than one product. And users of any one of its products don’t use all of its products. In effect, the company has a \textit{captive} audience. The company can run an ad in any of its products about the one or more other products that a user doesn’t use. Should it consider targeting—showing different (number of) ads to different users? There are five things to consider:

• Opportunity Cost: If the opportunity is limited, could the company make more profit by showing an ad about something else?
• The Cost of Showing an Ad to an Additional User: The cost of serving an ad; it is close to zero in the digital economy.
• The Cost of a Worse Product: As a result of seeing an irrelevant ad in the product, the user likes the product less. (The magnitude of the reduction depends on how disruptive the ad is and how irrelevant it is.) The company suffers in the end as its long-term profits are lower.
• Poisoning the Well: Showing an irrelevant ad means that people are more likely to skip whatever ad you present next. It reduces the company’s ability to pitch other products successfully.
• Profits: On the flip side of the ledger are expected profits. What are the expected profits from showing an ad? If you show a user an ad for a relevant product, they may not just buy and use the other product, but may also become less likely to switch from your stack. Further, they may even proselytize your product, netting you more users.

I formalize the problem here (pdf).

## Learning About [the] Loss (Function)

7 Nov

One of the things we often want to learn is the actual loss function people use for discounting ideological distance between self and a legislator. Often people try to learn the loss function using over actual distances. But if the aim is to learn the loss function, perceived distance rather than actual distance is better. It is so because perceived = what the voter believes to be true. People can then use the function to simulate out scenarios if perceptions = fact.

## Incentives to Care

11 Sep

A lot of people have their lives cut short because they eat too much and exercise too little. Worse, the quality of their shortened lives is typically much lower as a result of avoidable' illnesses that stem frombad behavior.’ And that isn’t all. People who are not feeling great are unlikely to be as productive as those who are. Ill-health also imposes a significant psychological cost on loved ones. The net social cost is likely enormous.

One way to reduce such costly avoidable misery is to invest upfront. Teach people good habits and psychological skills early on, and they will be less likely to self-harm.

So why do we invest so little up front? Especially when we know that people are ill-informed (about the consequences of their actions) and myopic.

Part of the answer is that there are few incentives for anyone else to care. Health insurance companies don’t make their profits by caring. They make them by investing wisely. And by minimizing ‘avoidable’ short-term costs. If a member is unlikely to stick with a health plan for life, why invest in their long-term welfare? Or work to minimize negative externalities that may affect the next generation?

One way to make health insurance care is to rate them on estimated quality-adjusted years saved due to interventions they sponsored. That needs good interventions and good data science. And that is an opportunity. Another way is to get the government to invest heavily early on to address this market failure. Another version would be to get the government to subsidize care that reduces long-term costs.

## Companies With Benefits or Potential Welfare Losses of Benefits

27 Sep

Many companies offer employees ‘benefits.’ These include paying for healthcare, investment plans, company gym, luncheons etc. (Just ask a Silicon Valley tech. employee for the full list.)

But why ‘benefits’? And why not cash?

A company offering a young man zero down health care plan seems a bit like within-company insurance. Post Obamacare—it also seems a bit unnecessary. (My reading of Obamacare is that it just mandates companies pay for the healthcare but doesn’t mandate that how they pay for it. So cash payments ought to be ok?)

For investment, the reasoning strikes me as thinner still. Let people decide what they want to do with their money.

In many ways, benefits look a bit like ‘gifts’—welfare reducing but widespread.

My recommendation: just give people cash. Or give them an option to have cash.

## Raising Money for Causes

10 Nov

Four teenagers, on the cusp of adulthood, and eminently well to do, were out on the pavement raising money for children struck with cancer. They had been out raising money for a couple of hours, and from a glance at their tin pot, I estimated that they had raised about $30 odd dollars, likely less. Assuming donation rate stays below$30/hr, or more than what they would earn if they were all working minimum wage jobs, I couldn’t help but wonder if their way of raising money for charity was rational; they could have easily raised more by donating their earnings from doing minimum wage job. Of course, these teenagers aren’t alone. Think of the people out in the cold raising money for the poor on New York pavements. My sense is that many people do not think as often about raising money by working at a “regular job”, even when it is more efficient (money/hour) (and perhaps even more pleasant). It is not clear why.

The same argument applies to those who run in marathons etc. to raise money. Preparing and running in marathon generally costs at least hundreds of dollars for an average ‘Joe’ (think about the sneakers, the personal trainers that people hire, the amount of time they `donate’ to train, which could have been spent working and donating that money to charity etc.). Ostensibly, as I conclude in an earlier piece, they must have motives beyond charity. These latter non-charitable considerations, at least at first glance, do not seem to apply to the case of teenagers, or to those raising money out in the cold in New York.

## Education and Economic Inequality

7 Dec

People seem to believe that increasing levels of education will reduce economic inequality. However, it isn’t clear if the policy is empirically supported. Here are some potential ways increasing levels of education can impact economic inequality:

1. As Grusky argues, the current high wage earners, whose high wages depend on education and lack of competition from similarly educated men and women (High Education Low Competition or HELCO) from similarly highly educated, will start earning a lower wage because of increased competition (thereby reducing inequality). This is assuming that HELCO won’t respond by trying to burnish their education credentials, etc. This is also assuming that HELCO exists as a large class. What likely exists is success attributable to networks, etc. That kind of advantage cannot be blunted by increasing education of those not in the network.
2. Another possibility is that education increases the number of high paying jobs available in the economy and it raises the boats of non-HELCO more than HELCO.
3. Another plausible scenario is that additional education produces only a modest effect with non-HELCO still mostly doing low paying jobs. This may due to only a modest increase in overall availability of ‘good jobs.’ Already easy access to education has meant that many a janitor and store clerk walk around with college degrees (see Why Did 17 Million Students Go to College?, and The Underemployed College Graduate).

Without an increase in ‘good jobs,’ the result of an increase in education is an increased heterogeneity in who succeeds (random draw at the extreme) but no change in the proportion of successful people. Or, increasing equality of opportunity (a commendable goal) but not reduction in economic inequality (though in a multi-generation game, it may even out). Increasing access to education also has the positive externality of producing a more educated society, another worthy goal.

How plentiful ‘good’ jobs are depends partly on how the economic activity is constructed. For instance, there may have once have been a case for only hiring one ‘super-talented person’ (say ‘superstar’) for a top-shelf job (say CEO). Now we have systems that can harness the wisdom of many. It is also plausible that that wisdom is greater than that of the superstar. It reasons then that the superstar is replaced; economic activity will be more efficient. Or else let other smart people who can contribute equally (if educated) be recompensed alternately for doing work that is ‘beneath them.’

## Social Science in a Guest Appearance in the Play, Capitalism

2 Jul

Social scientists are technology’s historians, anthropologists, sociologists, and scientists—measuring the social impact of technology. It is important to note that all this activity is forever doomed to survive in the echo chambers of the forgotten consciousness of society and consigned to only enter in casual desultory (or heated but always ineffectual) discussions. Social scientists also produce knowledge that is directly useful to Capitalism and there, of course, it plays a more important role.

Society is led by the 800-pound gorilla of Capitalism and the ‘logic’ of the market, which is quite separate from the ‘logic’ of social good, determines what is sold, how it is sold, and when. Social scientists merely study effects of new technologies as they are unleashed on the world. Universities open up new schools, departments, disciplines, and certainly new topics within disciplines as technology and reality march on. Take for example the following – two decades after television became a common amongst US households, David Phillips found evidence for ‘Werther effect’ like phenomena that linked suicides in real life to television suicides, and later still Robert Putnam linked heightened social alienation to television, and now there is a slew of literature detailing negative impact of violence on television. Of course, nobody ever thought that they might want to research the impact of something before it is released.

Social scientists are consigned to doing research that will only rarely wend its way to policymaking. And of course they will never get a chance to determine the course of technological growth, or other policies for those are tethered to Capitalism.

So what is the role of social science or for that matter science aside from helping create wealth, and helping society in a small number of cases where money making and social good coincide? There is really none in a world where increasingly the word regulation is seen as a plague.

Perhaps we can write our own epitaph â€“ we wagged our fingers and tongues, and scribbled furiously, as the chasm between the economic engine and the social good widened and Capitalism swallowed us whole. We also made some money doing that.