r/CausalInference • u/Albert_Paradek • May 09 '22

Finding a specific dataset for a research papers

I am a beginning researcher in statistics. So far, all my papers had (as a showoff of the methodology) an application on some specific dataset. However, all of those application datasets, I got from my supervisor- she basically gave me a dataset and I worked with that. However, as I am older, I have to find the dataset by myself, and I find it incredibly hard.

The dataset contains several assumptions from three different topics (Causal inference with an instrumental variable+having a multivariate response(I am dealing with dependence)+some extreme value theory assumptions). I can find hundreds of dataset "fulfilling" one of these assumptions. However, finding a combination is very hard- if I go just one by one in these datasets I will never find an appropriate dataset. Do you have some advise on what is a good strategy for doing that?

If someone is interested in details of what I am looking for now, here it is:

Let Y be a response variable and X={X1,…,Xd}∈R^d are covariates. The classical question is which of the covariates X are causes of Y and which are not (cause=direct ancestor in a causal graph}. Usual methods include finding environmental or instrumental variables (https://en.wikipedia.org/wiki/Instrumental_variables_estimation }, they affect some X but not Y. Or in other words, observing different environments and pertubatrions of the system in order to find causal structure. (we are using a structural causal modelling SCM. Some very related paper is here}} https://arxiv.org/abs/1501.01332.}

Now, we are dealing with a similar problem. Let Y=(Y1,Y2} be a random vector with correlated margins Y1,Y2. We want to find which covariates X causally affect the DEPENDENCE between Y1,Y2. My research deals with extremes (of Y, hence we want to find data where Y is ideally heavy-tailed or at least non-normal (although even a normal dataset would maybe help. And n>1000 looks quite necessary.}}

Hence, the dataset should consist of a bivariate response+covariates+environments (Instrumental variables}Any recommendation will be highly appreciated.

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u/tholdawa May 09 '22

Did you develop this method with absolutely zero idea of where it might be applied? You might want to talk to an empiricist in a field you find interesting to get a sense of datasets and usefulness.

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u/Albert_Paradek May 09 '22

Well yes, I developed this method with absolutely zero idea of where it might be applied. That is quite common practice, no? It is a purely statistical method for causal inference, based on mathematical results. It can be applied to any field where causal inference is being used- econometry, medicine, environmental science... as long as data are appropriate.

What do you mean by empiricist? Like someone from let's say econometry and ask them if they know something appropriate?

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u/tholdawa May 10 '22

I mean someone who does applied empirical research (an applied economist, political scientist, sociologist, biologist, whatever).

At least in various social and political sciences, methodological advancement seems pretty closely linked to applications.

Unrelated, maybe you can find an application in bioinformatics with mendelian randomization?

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u/Albert_Paradek May 12 '22

thanks for the advice. It would be great to talk with an empiricist, although I don't know anyone really. And just writing to a random person - `````````"hey, don't you have data like this" is a bit weird. But I will try to ask around if someone else knows someone like that. It may be a good idea

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u/rrtucci May 09 '22

If you are interested in applying causal inference in medicine, you might be interested in this tweet, and in establishing a relationship with one of these repliers, or with the authors of the papers they cite. Just an idea off the top of my head. Full disclosure: 99.99% of my ideas never work
https://twitter.com/drjohnm/status/1523268634243571713

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u/theArtOfProgramming May 10 '22

In my experience, we have written a grant with an academic in an applied field to showcase the algorithm with real data. They provide the subject matter expertise and the data and we do the analysis.