If you’ve been following posts here since the first of the year, you know that I’ve been writing about how I keep myself organized. Today I’m starting a completely different series in which I begin to collect my thoughts on how we can make judgments about the cause (or causes) of ecological phenomena1 and the circumstances under which judgments are possible. Before I start, I need to offer a few disclaimers.
- Any evolutionary biologist or ecologist who knows me and my work knows that it’s not uncommon for my ideas to represent a minority opinion. (Think pollen discounting for those of you who know my work on the evolution of plant mating systems.) I make no claim that anything I write here is broadly representative of what my fellow evolutionary biologists and ecologists think, only that it’s what I think. Please challenge me on anything you think I’ve got wrong, because I’m sure there will be things I get wrong, and the easiest way for me to discover those errors is for someone else to point them out.
- I had a minor in Philosophy as an undergraduate and there is an enormous literature on causality in the philosophy of science. I’ll be using a very crude understanding of “cause.” I don’t think it is wildly misleading, but I’m certain it wouldn’t stand up to serious scrutiny.2
- I’ll be thinking about causal inference in the specific context of trying to infer causes from observational data using statistics rather than from inferring causes controlled experiments.3 I’ll be using an approach developed in the 1970s by Donald Rubin, the Rubin Causal Model.4
- There is a very large literature on causal inference in the social sciences. I’ll be drawing heavily on Imbens and Rubin, Causal Inference for Statistics, Social and Biomedical Sciences: An Introduction,5 but there’s an enormous amount of material there that I won’t attempt to cover. I am also pretty new to the concepts associated with the Rubin causal model, so it’s entirely possible that I’ll misrepresent or misinterpret a point that the real experts got right. In other words, if something I say doesn’t make any sense, it’s more likely I got it wrong than that Imbens and Rubin got it wrong.
Although I will be thinking about causal inference in the context of observational data and statistics, I don’t plan to write much (if at all) about the problems with P-values, Bayes factors, credible/confidence intervals overlapping 0 (or not), and the like. If you’d like to know the concerns I have about them, here are links to old posts on those issues.
- Inference from noisy data with small samples
- Being Bayesian won’t save you
- Even an informative prior doesn’t help much
- Noisy data and small samples are a bad combination
- I’m calling the post “Causal inference in ecology” only because “Causal inference in ecology, evolutionary biology, and population genetics” would be too long. ↩
- There’s a good chance that a moderately competent undergraduate Philosophy major would find it woefully inadequate. ↩
- To be more precise, we don’t infer causes from controlled experiments. Rather, we have pre-existing hypotheses about possible causes, and we use controlled experiments to test those hypotheses. ↩
- In my relatively limited reading on the subject, I’ve most often seen it referred to as the Rubin causal model, but it is sometimes referred to as the Neyman causal model. ↩
- Reminder: If you click on that link, it will take you to Amazon.com. I use that link simply because it’s convenient. You can buy the book, if you’re so inclined, from many other outlets. I am not an Amazon affiliate, and I will not receive any compensation if you decide to buy the book regardless of whether you buy it at Amazon or elsewhere. By the way, Chapter 23 in Gelman and Hill’s book, Data Analysis Using Regression and Multilevel/Hierarchical Models has an excellent overview of the Rubin causal model. ↩