everywhere: meeting consumers where they are

1 Sep

Content delivery is not optimized for the technical stack used by an overwhelming majority of people. The technical stack of people who aren’t particularly tech-savvy, especially those who are old (over ~60 years), is often a messaging application like FB Messenger or WhatsApp. They currently do not have a way to ‘subscribe’ to Substack newsletters or podcasts or Youtube videos in the messaging application that they use (see below for an illustration of how this may look on the iPhone messaging app.) They miss content. And content producers have an audience hole.

Credit: Gaurav Gandhi

A lot of the content is distributed only via email or distributed within a specific application. There are good strategic reasons for that—you get to monitor consumption, recommend accordingly, control monetization, etc. But the reason why platforms like Substack, which enable independent content producers, limit distribution to email is not as immediately clear. It is unlikely a deliberate decision. It is likely a decision based on a lack of infrastructure that connects publishing to various messaging platforms. The future of messaging platforms is Slack—a platform that integrates as many applications as possible. As Whatsapp rolls out its business API, there is a potential to build an integration that allows producers to deliver premium content, leverage other kinds of monetization, like ads, and even build a recommendation stack. Eventually, it would be great to build that kind of integration for each of the messaging platforms, including iMessage, FB Messenger, etc.

Let me end by noting that there is something special about WhatsApp. No one has replicated the mobile phone-based messaging platform. And the idea of enabling a larger stack based on phone numbers remains unplumbed. Duo and FaceTime are great examples but there is potential for so much more. For instance, a calendar app. that runs on the mobile phone ID architecture.

The (Mis)Information Age: Provenance is Not Enough

31 Aug

The information age has bought both bounty and pestilence. Today, we are deluged with both correct and incorrect information. If we knew how to tell apart correct claims from incorrect, we would have inched that much closer to utopia. But the lack of nous in telling apart generally ‘obvious’ incorrect claims from correct claims has brought us close to the precipice of disarray. Thus, improving people’s ability to identify untrustworthy claims as such takes on urgency.

http://gojiberries.io/2020/08/31/the-misinformation-age-measuring-and-improving-digital-literacy/

Inferring the Quality of Evidence Behind the Claims: Fact Check and Beyond

One way around misinformation is to rely on an expert army that assesses the truth value of claims. However, assessing the truth value of a claim is hard. It needs expert knowledge and careful research. When validating, we have to identify with which parts are wrong, which parts are right but misleading, and which parts are debatable. All in all, it is a noisy and time-consuming process to vet a few claims. Fact check operations, hence, cull a small number of claims and try to validate those claims. As the rate of production of information increases, thwarting misinformation by checking all the claims seems implausibly expensive.

Rather than assess the claims directly, we can assess the process. Or, in particular, the residue of one part of the process for making the claim—sources. Except for claims based on private experience, e.g., religious experience, claims are based on sources. We can use the features of these sources to infer credibility. The first feature is the number of sources cited to make a claim. All else equal, the more number of sources saying the same thing, the greater the chances that the claim is true. None of this is to undercut a common observation: lots of people can be wrong about something. A harder test for veracity if a diverse set of people say the same thing. The third test is checking the credibility of the sources.

Relying on the residue is not a panacea. People can simply lie about the source. We want the source to verify what they have been quoted as saying. And in the era of cheap data, this can be easily enabled. Quotes can be linked to video interviews or automatic transcriptions electronically signed by the interviewee. The same system can be scaled to institutions. The downside is that the system may prove onerous. On the other hand, commonly, the same source is cited by many people so a public repository of verified claims and evidence can mitigate much of the burden.

But will this solve the problem? Likely not. For one, people can still commit sins of omission. For two, they can still draft things in misleading ways. For three, trust in sources may not be tied to correctness. All we have done is built a system for establishing provenance. And establishing the provenance is not enough. Instead, we need a system that incentivizes both correctness and presentation that makes correct interpretation highly likely. It is a high bar. But it is the bar—correct and liable to correctly interpreted.

To create incentives for publishing correct claims, we need to either 1. educate the population, which brings me to the previous post, or 2. find ways to build products and recommendations that incentivize correct claims. We likely need both.

The (Mis)Information Age: Measuring and Improving ‘Digital Literacy’

31 Aug

The information age has bought both bounty and pestilence. Today, we are deluged with both correct and incorrect information. If we knew how to tell apart correct claims from incorrect, we would have inched that much closer to utopia. But the lack of nous in telling apart generally ‘obvious’ incorrect claims from correct claims has brought us close to the precipice of disarray. Thus, improving people’s ability to identify untrustworthy claims as such takes on urgency.

Before we find fixes, it is good to measure how bad things are and what things are bad. This is the task the following paper sets itself by creating a ‘digital literacy’ scale. (Digital literacy is an overloaded term. It means many different things, from the ability to find useful information, e.g., information about schools or government programs, to the ability to protect yourself against harm online (see here and here for how frequently people’s accounts are breached and how often they put themselves at risk of malware or phishing), to the ability to identify incorrect claims as such, which is how the paper uses it.)

Rather than build a skill assessment kind of a scale, the paper measures (really predicts) skills indirectly using some other digital literacy scales, whose primary purpose is likely broader. The paper validates the importance of various constituent items using variable importance and model fit kinds of measures. There are a few dangers of doing that:

  1. Inference using surrogates is dangerous as the weakness of surrogates cannot be fully explored with one dataset. And they are liable not to generalize as underlying conditions change. We ideally want measures that directly measure the construct.
  2. Variable importance is not the same as important variables. For instance, it isn’t clear why “recognition of the term RSS,” the “highest-performing item by far” has much to do with skill in identifying untrustworthy claims.

Some other work builds uncalibrated measures of digital literacy (conceived as in the previous paper). As part of an effort to judge the efficacy of a particular way of educating people about how to judge untrustworthy claims, the paper provides measures of trust in claims. The topline is that educating people is not hard (see the appendix for the description of the treatment). A minor treatment (see below) is able to improve “discernment between mainstream and false news headlines.”

Understandably, the effects of this short treatment are ‘small.’ The ITT short-term effect in the US is: “a decrease of nearly 0.2 points on a 4-point scale.” Later in the manuscript, the authors provide the substantive magnitude of the .2 pt net swing using a binary indicator of perceived headline accuracy: “The proportion of respondents rating a false headline as “very accurate” or “somewhat accurate” decreased from 32% in the control condition to 24% among respondents who were assigned to the media literacy intervention in wave 1, a decrease of 7 percentage points.” The .2 pt. net swing on a 4 point scale leading to a 7% difference is quite remarkable and generally suggests that there is a lot of ‘reverse’ intra-category movement that the crude dichotomization elides over. But even if we take the crude categories as the quantity of interest, a month later in the US, the 7 percent swing is down to 4 percent:

“…the intervention reduced the proportion of people endorsing false headlines as accurate from 33 to 29%, a 4-percentage-point effect. By contrast, the proportion of respondents who classified mainstream news as not very accurate or not at all accurate rather than somewhat or very accurate decreased only from 57 to 55% in wave 1 and 59 to 57% in wave 2.

Guess et al. 2020

The opportunity to mount more ambitious treatments remains sizable. So does the opportunity to more precisely understand what aspects of the quality of evidence people find hard to discern. And how we could release products that make their job easier.

Another ANES Goof-em-up: VCF0731

30 Aug

By Rob Lytle

At this point, it’s well established that the ANES CDF’s codebook is not to be trusted (I’m repeating “not to be trusted to include a second link!). Recently, I stumbled across another example of incorrect coding in the cumulative data file, this time in VCF0731 – Do you ever discuss politics with your family or friends?

The codebook reports 5 levels:

Do you ever discuss politics with your family or friends?

1. Yes
5. No

8. DK
9. NA

INAP. question not used

However, when we load the variable and examine the unique values:

# pulling anes-cdf from a GitHub repository
cdf <- rio::import("https://github.com/RobLytle/intra-party-affect/raw/master/data/raw/cdf-raw-trim.rds")


unique(cdf$VCF0731)
## [1] NA  5  1  6  7

We see a completely different coding scheme. We are left adrift, wondering “What is 6? What is 7?” Do 1 and 5 really mean “yes” and “no”?

We may never know.

For a survey that costs several million dollars to conduct, you’d think we could expect a double-checked codebook (or at least some kind of version control to easily fix these things as they’re identified).

AFib: Apple Watch Did Not Increase Atrial Fibrillation Diagnoses

28 Aug

A new paper purportedly shows that the release of Apple Watch 2018 which supported ECG app did not cause an increase in AFib diagnoses (mean = −0.008). 

They make the claim based on 60M visits from and 1270 practices across 2 years.

Here are some things to think about:

  1. Expected effect size. Say the base AF rate as .41%. Let’s say 10% has the ECG app + Apple watch. (You have to make some assumptions about how quickly people downloaded the app. I am making a generous assumption that 10% do it the day of release.) For the 10%, say it is .51%. Add’l diagnoses expected = .01*30M ~ 3k.
  2. Time trend. 2018-19 line is significantly higher (given the baseline) than 2016-2017. It is unlikely to be explained by the aging of the population. Is there a time trend? What explains it? More acutely, diff. in diff. doesn’t account for that.
  3. Choice of the time period. When you have observations over multiple time periods pre-treatment and post-treatment, the inference depends on which time period you use. For instance,  if I do an “ocular distortion test”, the diff. in diff. with observations from Aug./Sep. would suggest a large positive impact. For a more transparent account of assumptions, see diff.healthpolicydatascience.org (h/t Kyle Foreman).
  4. Clustering of s.e. Some correlation in diagnosis because of facility (doctor) which is unaccounted for.

Survey Experiments With Truth: Learning From Survey Experiments

27 Aug

Tools define science. Not only do they determine how science is practiced but also what questions are asked. Take survey experiments, for example. Since the advent of online survey platforms, which made conducting survey experiments trivial, the lure of convenience and internal validity has persuaded legions of researchers to use survey experiments to understand the world.

Conventional survey experiments are modest tools. Paul Sniderman writes,

“These three limitations of survey experiments—modesty of treatment, modesty of scale, and modesty of measurement—need constantly to be borne in mind when brandishing term experiment as a prestige enhancer.” I think we can easily collapse these in two — treatment (which includes ‘scale’ as he defines it— the amount of time) and measurement.

Paul Sniderman

Note: We can collapse these three concerns into two— treatment (which includes ‘scale’ as Paul defines it— the amount of time) and measurement.

But skillful artisans have used this modest tool to great effect. Famously, Kahneman and Tversky used survey experiments, e.g., Asian Disease Problem, to shed light on how people decide. More recently, Paul Sniderman and Tom Piazza have used survey experiments to shed light on an unsavory aspect of human decision making: discrimination. Aside from shedding light on human decision making, researchers have also used survey experiments to understand what survey measures mean, e.g., Ahler and Sood

The good, however, has come with the bad; insight has often come with irreflection. In particular, Paul Sniderman implicitly points to two common mistakes that people make:

  1. Not Learning From the Control Group. The focus on differences in means means that we sometimes fail to reflect on what the data in the Control Group tells us about the world. Take the paper on partisan expressive responding, for instance. The topline from the paper is that expressive responding explains half of the partisan gap. But it misses the bigger story—the partisan differences in the Control Group are much smaller than what people expect, just about 6.5% (see here). (Here’s what I wrote in 2016.)
  2. Not Putting the Effect Size in Context. A focus on significance testing means that we sometimes fail to reflect on the modesty of effect sizes. For instance, providing people $1 for a correct answer within the context of an online survey interview is a large premium. And if providing a dollar each on 12 (included) questions nudges people from an average of 4.5 correct responses to 5, it suggests that people are resistant to learning or impressively confident that what they know is right. Leaving $7 on the table tells us more than the .5, around which the paper is written. 

    More broadly, researchers are obtuse to the point that sometimes what the results show is how impressively modest the movement is when you ratchet up the dosage. For instance, if an overwhelming number of African Americans favor Whites who have scored just a few points more than a Black student, it is a telling testament to their endorsement of meritocracy.

Amartya Sen on Keynes, Robinson, Smith, and the Bengal Famine

17 Aug

Sen in conversation with Angus Deaton and Tim Besleypdf and video.

Excepts:

On Joan Robinson

“She took a position—which has actually become very popular in India
now, not coming from the left these days, but from the right—that what you have to concentrate on is simply maximizing economic growth. Once you have grown and become rich, then you can do health care, education, and all this other stuff. Which I think is one of the more profound errors that you can make in development planning. Somehow Joan had a lot of sympathy for that position. In fact, she strongly criticized Sri Lanka for offering highly subsidized food to everyone on nutritional grounds. I remember the phrase she used: “Sri Lanka is trying to taste the fruit of
the tree without growing it.”

Amartya Sen

On Keynes:

“On the unemployment issue I may well be, but if I compare an economist
like Keynes, who never took a serious interest in inequality, in poverty, in the environment, with Pigou, who took an interest in all of them, I don’t think I would be able to say exactly what you are asking me to say.”

Amartya Sen

On the 1943 Bengal Famine, the last big famine in India in which ~ 3M people perished:

“Basically I had figured out on the basis of the little information I had (that indeed
everyone had) that the problem was not that the British had the wrong data, but that their theory of famine was completely wrong. The government was claiming that there was so much food in Bengal that there couldn’t be a famine. Bengal, as a whole, did indeed have a lot of food—that’s true. But that’s supply; there’s also demand, which was going up and up rapidly, pushing prices sky-high. Those left behind in a boom economy—a boom generated by the war—lost out in the competition for buying food.”

“I learned also—which I knew as a child—that you could have a famine with a lot of food around. And how the country is governed made a difference. The British did not want rebellion in Calcutta. I believe no one of Calcutta died in the famine. People died in Calcutta, but they were not of Calcutta. They came from elsewhere, because what little charity there was came from Indian businessmen based in Calcutta. The starving people
kept coming into Calcutta in search of free food, but there was really not much of that. The Calcutta people were entirely protected by the Raj to prevent discontent of established people during the war. Three million people in Calcutta had ration cards, which entailed that at least six million people were being fed at a very subsidized price of food. What the government did was to buy rice at whatever price necessary to purchase it in the rural areas, making the rural prices shoot up. The price of rationed food in Calcutta for established residents was very low and highly subsidized, though the market price in Calcutta—outside the rationing network—rose with the rural price increase.”

Amartya Sen

On John Smith

“He discussed why you have to think pragmatically about the different institutions to be combined together, paying close attention to how they respectively work. There’s a passage where he’s asking himself the question, Why do we strongly want a good political economy? Why is it important? One answer—not the only one—is that it will lead to high economic growth (this is my language, not Smith’s). I’m not quoting his words, but he talks about the importance of high growth, high rate of progress. But why is that important? He says it’s important for two distinct reasons. First, it gives the individual more income, which in turn helps people to do what they would value doing. Smith is talking here about people having more capability. He doesn’t use the word capability, but that’s what he is talking about here. More income helps you to choose the kind of life that you’d like to lead. Second, it gives the state (which he greatly valued as an institution when properly used) more revenue, allowing it to do those things which only the state can do well. As an example, he talks about the state being able to provide free school education.”

Amartya Sen

Nothing to See Here: Statistical Power and “Oversight”

13 Aug

“Thus, when we calculate the net degree of expressive responding by subtracting the acceptance effect from the rejection effect—essentially differencing off the baseline effect of the incentive from the reduction in rumor acceptance with payment—we find that the net expressive effect is negative 0.5%—the opposite sign of what we would expect if there was expressive responding. However, the substantive size of the estimate of the expressive effect is trivial. Moreover, the standard error on this estimate is 10.6, meaning the estimate of expressive responding is essentially zero.

https://journals.uchicago.edu/doi/abs/10.1086/694258

(Note: This is not a full review of all the claims in the paper. There is more data in the paper than in the quote above. I am merely using the quote to clarify a couple of statistical points.)

There are two main points:

  1. The fact that estimate is close to zero and the s.e. is super fat are technically unrelated. The last line of the quote, however, seems to draw a relationship between the two.
  2. The estimated effect sizes of expressive responding in the literature are much smaller than the s.e. Bullock et al. (Table 2) estimate the effect of expressive responding at about 4% and Prior et al. (Figure 1) at about ~ 5.5% (“Figure 1(a) shows, the model recovers the raw means from Table 1, indicating a drop in bias from 11.8 to 6.3.”). Thus, one reasonable inference is that the study is underpowered to reasonably detect expected effect sizes.

Casual Inference: Errors in Everyday Causal Inference

12 Aug

Why are things the way they are? What is the effect of something? Both of these reverse and forward causation questions are vital.

When I was at Stanford, I took a class with a pugnacious psychometrician, David Rogosa. David had two pet peeves, one of which was people making causal claims with observational data. And it is in David’s class that I learned the pejorative for such claims. With great relish, David referred to such claims as ‘casual inference.’ (Since then, I have come up with another pejorative phrase for such claims—cosal inference—as in merely dressing up as causal inference.)

It turns out that despite its limitations, casual inference is quite common. Here are some fashionable costumes:

  1. 7 Habits of Successful People: We have all seen business books with such titles. The underlying message of these books is: adopt these habits, and you will be successful too! Let’s follow the reasoning and see where it falls apart. One stereotype about successful people is that they wake up early. And the implication is you wake up early you can be successful too. It *seems* right. It agrees with folk wisdom that discomfort causes success. But can we reliably draw inferences about what less successful people should do based on what successful people do? No. For one, we know nothing about the habits of less successful people. It could be that less successful people wake up *earlier* than the more successful people. Certainly, growing up in India, I recall daily laborers waking up much earlier than people living in bungalows. And when you think of it, the claim that servants wake up before masters seems uncontroversial. It may even be routine enough to be canonized as a law—the Downtown Abbey law. The upshot is that when you select on the dependent variable, i.e., only look at cases where the variable takes certain values, e.g., only look at the habits of financially successful people, even correlation is not guaranteed. This means that you don’t even get to mock the claim with the jibe that “correlation is not causation.”

    Let’s go back to Goji’s delivery service for another example. One of the ‘tricks’ that we had discussed was to sample failures. If you do that, you are selecting on the dependent variable. And while it is a good heuristic, it can lead you astray. For instance, let’s say that most of the late deliveries our early morning deliveries. You may infer that delivering at another time may improve outcomes. Except, when you look at the data, you find that the bulk of your deliveries are in the morning. And the rate at which deliveries run late is *lower* early morning than during other times.

    There is a yet more famous example of things going awry when you select on the dependent variable. During World War II, statisticians were asked where the armor should be added on the planes. Of the aircraft that returned, the damage was concentrated in a few areas, like the wings. The top-of-head answer is to suggest we reinforce areas hit most often. But if you think about the planes that didn’t return, you get to the right answer, which is that we need to reinforce areas that weren’t hit. In literature, people call this kind of error, survivorship bias. But it is a problem of selecting on the dependent variable (whether or not a plane returned) and selecting on planes that returned.

  2. More frequent system crashes cause people to renew their software license. It is a mistake to treat correlation as causation. There are many different reasons behind why doing so can lead you astray. The rarest reason is that lots of odd things are correlated in the world because of luck alone. The point is hilariously illustrated by a set of graphs showing a large correlation between conceptually unrelated things, e.g., there is a large correlation between total worldwide non-commercial space launches and the number of sociology doctorates that are awarded each year.

    A more common scenario is illustrated by the example in the title of this point. Commonly, there is a ‘lurking’ or ‘confounding’ variable that explains both sides. In our case, the more frequently a person uses a system, the more the number of crashes. And it makes sense that people who use the system most frequently also need the software the most and renew the license most often.

    Another common but more subtle reason is called Simpson’s paradox. Sometimes the correlation you see is “wrong.” You may see a correlation in the aggregate, but the correlation runs the opposite way when you break it down by group. Gender bias in U.C. Berkeley admissions provides a famous example. In 1973, 44% of the men who applied to graduate programs were admitted, whereas only 35% of the women were. But when you split by department, which eventually controlled admissions, women generally had a higher batting average than men. The reason for the reversal was women applied more often to more competitive departments, like—-wait for it—-English and men were more likely to apply to less competitive departments like Engineering. None of this is to say that there isn’t bias against women. It is merely to point out that the pattern in aggregated data may not hold when you split the data into relevant chunks.

    It is also important to keep in mind the opposite of correlation is not causation—lack of correlation does not imply a lack of causation.

  3. Mayor Giuliani brought the NYC crime rate down. There are two potential errors here:
    • Forgetting about ecological trends. Crime rates in other big US cities went down at the same time as they did in NY, sometimes more steeply. When faced with a causal claim, it is good to check how ‘similar’ people fared. The Difference-in-Differences estimator that builds on this intuition.
    • Treating temporally proximate as causal. Say you had a headache, you took some medicine and your headache went away. It could be the case that your headache went away by itself, as headaches often do.

  4. I took this homeopathic medication and my headache went away. If the ailments are real, placebo effects are a bit mysterious. And mysterious they may be but they are real enough. Not accounting for placebo effects misleads us to ascribe the total effect to the medicine. 

  5. Shallow causation. We ascribe too much weight to immediate causes than to causes that are a few layers deeper.

  6.  Monocausation: In everyday conversations, it is common for people to speak as if x is the only cause of y.

  7.  Big Causation: Another common pitfall is reading x causes y as x causes y to change a lot. This is partly a consequence of mistaking statistical significance with substantive significance, and partly a consequence of us not paying close enough attention to numbers.

  8. Same Effect: Lastly, many people take causal claims to mean that the effect is the same across people. 

Routine Maintenance: How to Build Habits

11 Aug

With Mark Paluta

Building a habit means trying to maximize the probability of doing something at some regular cadence.

max [P(do the thing)]

This is difficult because we have time-inconsistent preferences. When asked if we would prefer to run or watch TV next Wednesday afternoon, we are more likely to say run. Arrive Wednesday, and we are more likely to say TV.

Willpower is a weak tool for most of us, so we are better served thinking systematically about what conditions maximize the probability of doing the thing we plan to do. The probability of doing something can be modeled as a function of accountability, external motivation, friction, and awareness of other mental tricks:

P(do the thing) ~ f(accountability, external motivation, friction, other mental tricks)

Accountability: To hold ourselves accountable, at the minimum, we need to transparently record data. Without an auditable record of performance, we are liable to either turn a blind eye to failures or to rationalize them away. There are a couple of ways to amplify accountability pressures:

  • Social Pressure: We do not want to embarrass ourselves in front of people we know. This pressures us to do the right thing. So record your commitments and how you follow-up on them publicly. Or make a social commitment. “Burn the boats” and tell all your friends you are training for a marathon.
  • Feel the Pain: Donate to an organization you dislike whenever you fail.
  • Enjoy the Rewards: The flip side of feeling the pain is making success sweeter. One way to do that is to give yourself a nice treat if you finish X days of Y.
  • Others Are Counting on You: If you have a workout partner, you are more likely to go because you want to come through for your friend (besides it is more enjoyable to do the activity with someone you like). 
  • Redundant Observation Systems: You can’t just rely on yourself to catch yourself cheating (or just failing). If you have a shared fitness worksheet, others will notice that you missed a day. They can text you a reminder. Automated systems like what we have on the phone are great as well.

Rely on Others: We can rely on our friends to motivate us. One way to capitalize on that is to create a group fitness spreadsheet and to encourage each other. For instance, if your friend did not fill in yesterday’s workout, you can text them a reminder or a motivational message.

NudgeReduce frictions for doing the planned activity. For example, place your phone outside your bedroom before bed or sleep in your running clothes.

Other Mental Tricks: There are two other helpful mental models for building habits. One is momentum, and the other is error correction. 

Momentum: P(do the thingt+1 | do the thing_t)

Error correction: P(do the thing_t+1 | !do the thing_t)

The best way to build momentum is to track streaks (famously used by Jerry Seinfeld). Not only do you get a reward every time you successfully complete the task, but the longer your streak, the less you want to break it.

Error correction on the other hand is turning a failure into motivation. Don’t miss two days in a row. Failure is part of the process, but do not let it compound. View the failure as step 0 of the next streak.