Uncommon Ground

Monthly Archive: April 2018

Made it to Brisbane

It’s among the longest stretch of (planned) travel that I’ve done.1 I

  • left Hartford at 11:45am EDT on Thursday, April 19
  • arrived in Cinncinnati at 1:53pm,
  • left Cinncinnati at 2:45pm,
  • arrived in Los Angeles at 4:45pm PDT,
  • left Los Angeles at 10:30, and
  • arrived in Brisbane at 5:30am Australian Eastern time on Saturday, April 21.

There’s a 14 hour time difference between Brisbane and Hartford. That makes the total travel time 27 hours, 45 minutes gate to gate. I arrived at my hotel about 2 1/2 hours ago. Remarkably, they had a room they could give me, even though the official check in time isn’t until 2:00pm. It’s a very comfortable room in what appears to be a very nice part of the city. I don’t have any meetings until tomorrow. Once I’ve finished up a couple of things I want to do, I’m going to put on some comfortable shoes and go for a walk around town with my camera. First stop, the City Botanic Gardens. I’ll miss the farmer’s market, which is held tomorrow, and I’m not sure what I’ll visit after the botanic gardens, but I’m going to keep going all day. If I can go to bed at something resembling a normal time, there’s a good chance I’ll escape the worst effects of jet lag tomorrow.

The photo is the view from my hotel room.

  1. A few years ago it took me 3 1/2 days to get home from Capetown. I was stranded in Amsterdam for 2 nights. Yes I mean stranded. The first night I was stuck in the airport. The second night I was at an airport hotel, but I didn’t get there until 3 in the afternoon – too late to go into the city and enjoy anything.

On my way to Brisbane

UConn is a member of Universitas 21, an international group of universities dedicated to excellence in research and education. Every year Deans and Directors of Graduate Studies (DDoGS) of U21 universities gather at one of the member universities to exchange ideas about improving graduate education. This year the discussions will include sessions on providing support for career planning, advising and mentoring graduate students and postdocs, and entrepreneurship. The meetings begin Sunday morning and continue through Tuesday – three very full days of vigorous discussion.

This year, the University of Queensland is hosting the meeting, hence my travel to Brisbane. I’m sitting in the departure lounge at Bradley as I type this, and I expect to land in Brisbane about 36 hours from now. If time permits, I’ll send an update or two from the DDoGS meeting. If not, expect a short report after I return.

Causal inference in ecology – Counterfactuals

Causal inference in ecology – links to the series

Let’s start with a few preliminaries.1

  • A causal factor (“cause” for short) is something that is predictably related to a particular outcome. For example, fertilizing crops generally increases their yield, so fertilizer is a causal factor related to yield. The way I think about it, a causal factor need not always lead to the outcome. It’s enough if it merely increases the probability of the outcome. For example, smoking doesn’t always lead to lung cancer among those who smoke, but it does increase the probability that you will suffer from lung cancer if you smoke.
  • Causes precede effects.2 That’s one reason why teleology is problematic. A teleological explanation explains the current state of things as a result of, i.e., as caused by, something in the future, namely a purpose.3
  • Effects may have multiple causes. The world, or at least the world of biology, is a complicated place. Regardless of what phenomenon you’re studying, there are likely to be several (or many) causal factors that influence.

The last point is one of the most important ones for purposes of this series. When we are investigating a phenomenon,4 we’re trying to discern which of several plausible causal factors plays a role and, possibly, the relative “importance” of those causal factors.5

To make this concrete, let’s suppose that we’re trying to determine whether application of nitrogen fertilizer increases the yield of corn. That means we have to determine whether adding nitrogen and adding nitrogen alone increases corn yield. Why the emphasis on “adding nitrogen alone”? Suppose that we added nitrogen to a corn field by adding manure. Then increases in the amount of applied nitrogen are associated with increases in the amount of a host of other substances. If yields increased, we’d know that adding manure increases yield, but not whether it’s because of the nitrogen in manure or something else. Why does this matter?

From very early on in our education we’re taught that “correlation is not the same as causation.” We want to distinguish cases where A causes B from cases where A is merely correlated with B. Yet, as David Hume pointed out long ago, experience6 alone can only show us that A and B actually occur together, not that they must occur together (link). One way of distinguishing cause from correlation is that causes support counterfactual statements. They provide us with a reason to believe statements like “If we had applied nitrogen to the field, the corn yield would have increased” even if we never applied nitrogen to the field at all. The only reason I can see that we could believe such a statement is if we had already determined that adding nitrogen and adding nitrogen alone increases corn yield.7

How do we determine that? Randomized controlled experiments are the most widely known approach, and they are typically regarded as the gold standard against which all other means of inference are compared. That’s where we’ll pick up in the next installment.

  1. As I warned in the introduction to the series, I am not an expert in causal inference. The terminology I use is likely both to be imprecise and to be somewhat different from the terminology experts use.
  2. Philosophers have argued about whether backward causation is possible, but I’m going to ignore that possibility.
  3. Biologists sometimes use teleological language to explain adaptation, e.g., land animals evolved legs to provide mobility. It is, however, relatively easy (if a bit long-winded) to eliminate the teleological language, because natural selection shows how adaptations arise from differential reproduction and survival (link).
  4. Or at least this is how it is when I’m investigating a phenomenon.
  5. I’ll come back to the idea of identifying the relative importance of causal factors in a future post.
  6. Or experiment.
  7. If there are any philosophers reading this, you’ll recognize that this account is horribly sketchy and amounts to little more than proof by vigorous assertion. If you’re so inclined, I invite you to flesh out more complete explanations for readers who are interested.

Causal inference in ecology – Introduction to the series

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.

  1. I’m calling the post “Causal inference in ecology” only because “Causal inference in ecology, evolutionary biology, and population genetics” would be too long.
  2. There’s a good chance that a moderately competent undergraduate Philosophy major would find it woefully inadequate.
  3. 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.
  4. 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.
  5. 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.

Getting organized in 2018 – Putting it all together

Getting organized in 2018 – links to the series

When I started this series I didn’t think it would take me three months to finish, but it did. If you’ve been following along, you’ve read about how I keep myself organized. In this last post, I’ll put it altogether by running through the process with links to the individual steps. If you’re familiar with David Allen’s Getting Things Done, this will look pretty familiar, although I discovered most of these practices on my own well before I read his book.1

It all starts on Sunday morning. I brew myself a nice cup of coffee – black, no sugar -, sit down at my laptop, and boot up OmniFocus. I move tasks that may have accumulated in my OmniFocus Inbox to the appropriate Project folder or subfolder.2Then I use the Review perspective to review all of my tasks. I’ve set different projects for different review frequencies. Some I review every week, some I review once a month, and some I review only once every 3-6 months. But everything gets reviewed at a frequency experience has taught me is appropriate. Every week the review will review tasks that need to be rescheduled (sometimes earlier, sometimes later) or dropped. And every week the review gives me ideas for new tasks or projects that get entered into the appropriate place (sometimes it’s Someday/Maybe for things that I just need to think about, sometimes it’s a new project or a new task in an existing project). With that review done, I’m confident that I’ve planned for anything I can plan for in the following week and that my complete list of projects and tasks is in good order so that I’ll be prompted about other important things when the right time arrives.

I review my calendar for the week ahead at the same time. Before I became a dean, I made appointments with myself for blocks of time that I could use for focused work. I treated those time blocks as real appointments and did my best not to let other commitments break them up. As a Dean, I can’t be that inflexible. Too many things arise that need prompt, if not immediate, attention. I’ve cut back on scheduling blocks of time for focused work. Only when I have a really important project that has a looming deadline, a grant proposal for example, will I put a “Do not disturb” block of time on my calendar with instructions to my administrative assistant to check with me before scheduling anything short of a meeting request from the President or the Provost in that time block. That’s as close as I can get to planning deep work time ahead of time. Mostly, I have to take advantage of time blocks when they appear, and they are rarely more than a couple of hours.

On any given day, my calendar and OmniFocus keep me on track. Some of my OmniFocus tasks have specific times of day associated with them, meeting preparation for example. Many have only the end of the day, 5:00pm. I review today’s task list every morning. As a result, I can often pick something to do without checking OmniFocus first, but I do check it frequently throughout the day, often because I’m entering something new that just came up.

At meetings I rarely take paper. I’ve either saved the electronic versions of documents that were sent or scanned paper versions to PDF. Either way, any documents I have before the meeting are in Dropbox, Evernote, or both. Any notes I’ve made before the meeting were probably made with Emacs using Markdown, and published to Evernote with Byword. At the meetings I use pen and paper, my everything notebook. At the end of the day, I’ll scan notes to PDF and save them to Dropbox or I’ll scan them directly to Evernote. As I wrote earlier, I don’t have a clear plan for what goes to Dropbox and what goes to Evernote, but either way I can get it from any electronic device I have handy. If there are action items I need to follow up on, I will have marked them with an arrow (==>) in my notebook, and I transfer them to OmniFocus. I also check over my everything notebook during my weekly review to make sure I haven’t missed any action items that need to be recorded.

Writing it all out like this may make it sound pretty time consuming and complicated, but it’s not. The daily task management is a natural part of the activity and it doesn’t add any time. It just uses the time differently. The weekly review takes a bit longer, but spending 15 minutes or half an hour with a nice cup of coffee looking over the week to come is a nice way to spend a quiet Sunday morning.

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