I was at a very interesting workshop today on adaptive interventions. Most of the folks at the workshop design interventions for chronic conditions and would be used to testing their interventions using a randomized trial.

Much of the discussion was on heterogeneity of treatment effects. In fact, much of their research is based on the premise that individualized treatments should do better than giving everyone the same treatment. Of course, the average treatment might be the best course for everyone, but they have certainly found applications where this is not true. It seems that many more could be found.

I started to think about applications in the survey realm. We do have the concept of tailoring, which began in our field with research into survey introductions. But do we use it much? I have two feelings on this question. No, there aren't many examples like the article I linked to above. We usually test interventions (design features like incentives, letters, etc.) on the whole sample. We may note that they work differentially across subgroups, but we rarely design interventions for specific subgroups.

My other feeling is that, yes, we do some of this. For example, we only apply refusal conversions to cases that have refused. We just need to think about all of the things that we do and maybe 'relabel' them.

The other thought that I had was that it would be difficult for us to design completely individualized treatments like I saw them doing today. We don't get the same kind of detailed feedback that they get. But still, I think we can move toward more differentiated treatment strategies.

### "Responsive Design" and "Adaptive Design"

My dissertation was entitled "Adaptive Survey Design to Reduce Nonresponse Bias." I had been working for several years on "responsive designs" before that. As I was preparing my dissertation, I really saw "adaptive" design as a subset of responsive design.

Since then, I've seen both terms used in different places. As both terms are relatively new, there is likely to be confusion about the meanings. I thought I might offer my understanding of the terms, for what it's worth.

The term "responsive design" was developed by Groves and Heeringa (2006). They coined the term, so I think their definition is the one that should be used. They defined "responsive design" in the following way:

1. Preidentify a set of design features that affect cost and error tradeoffs.
2. Identify indicators for these costs and errors. Monitor these during data collection.
3. Alter the design features based on pre-identified decision rules based on the indi…

### An Experimental Adaptive Contact Strategy

I'm running an experiment on contact methods in a telephone survey. I'm going to present the results of the experiment at the FCSM conference in November. Here's the basic idea.

Multi-level models are fit daily with the household being a grouping factor. The models provide household-specific estimates of the probability of contact for each of four call windows. The predictor variables in this model are the geographic context variables available for an RDD sample.

Let $\mathbf{X_{ij}}$ denote a $k_j \times 1$ vector of demographic variables for the $i^{th}$ person and $j^{th}$ call. The data records are calls. There may be zero, one, or multiple calls to household in each window. The outcome variable is an indicator for whether contact was achieved on the call. This contact indicator is denoted $R_{ijl}$ for the $i^{th}$ person on the $j^{th}$ call to the $l^{th}$ window. Then for each of the four call windows denoted $l$, a separate model is fit where each household is assum…

### Goodhart's Law

I enjoy listening to the data skeptic podcast. It's a data science view of statistics, machine learning, etc. They recently discussed Goodhart's Law on the podcast. Goodhart's was an economist. The law that bears his name says that "when a measure becomes a target, then it ceases to be a good measure." People try and find a way to "game" the situation. They maximize the indicator but produce poor quality on other dimensions as a consequence. The classic example is a rat reduction program implemented by a government. They want to motivate the population to destroy rats, so they offer a fee for each rat that is killed. Rather than turn in the rat's body, they just ask for the tail. As a result, some persons decide to breed rats and cut off their tails. The end result... more rats.

I have some mixed feelings about this issue. There are many optimization procedures that require some single measure which can be either maximized or minimized. I think thes…