As someone who watched far too many people slowly die when I was much younger than that lesson is usually learned…,

Agreed. Completely agreed.

I am not afraid of being dead. I am afraid of the painful journey that concludes in death. I am terrified of a painful death. I have seen “passed in their sleep”.

None of it is as smooth and painless as we pretend. I am convinced that we don’t show what death actually looks like because we couldn’t function if everyone truly saw it at a young age.

Since I've been put under for surgery, I've known what not existing is like. I'm not afraid being dead, just of the road that leads there.

What article is not a single point of view? What even is this objection. You write like a bot.

>you can never infer causality from looking passively at data

In this view, causal inference is relegated only to a single observation. Extrapolating results to any other similar expiremental set-up (even identical) is just that. To quote Hume,

>I say, then, that, even after we have experience of the operation of cause and effect, our conclusions from that experience are not founded on any reasoning, or any process of the understanding

There is an epistemological limit of the concept of causation. In statistical inference, based on probability theory, a good professor will use this limit to routinely used to smack undergrads upside the head- be it regression or p-values.

We assume distributions of underlying samples, along with central limit theorem to do statistics that support causal inference. We can attempt to control for type 1 error via our set-up, but even when our assumptions are not violated we still can never claim a result with 100% certainty.

Carefully controlled experimentation is better than using some observation set, but it suffers two drawbacks- it is expensive to obtain and it's uses beyond the experiment are quite limited, necessarily requiring extrapolation. So I argue pragmatically that we should use latent data and the statistical tools at our disposal to understand causation (to the extent it actually exists) with the appropriate limiting caveats.

>In his book, The Case Against Death previous NYU philosophy professor, Ingemar Patrick Linden, veers away from the predominant philosophical notion that we should find ways to accept death as natural and inevitable and see it for what it is: 'simply awful.'

I'm with Ingemar Linden on this one. If something is a problem, "accept the problem" isn't a solution. Instead, you should try to find the root cause, and eliminate (or, at least mitigate) the problem.

Like this guy:

https://en.wikipedia.org/wiki/David_A._Sinclair

>He has expressed the view that advances in aging research could enable humans to live to be 200 years old.

Is he right? Will he succeed in moving towards that goal? Beats me. But he's not wrong for trying.

Anyway, the rest of the article seems to be just different ways to find ways to accept death as natural and inevitable. Which is solving a different problem: Not the problem of death itself, but the problem of dealing with death.

Solve the one and you (in large part) solve the other, too.

The article is a single point of view and thus flawed. From the stand point of a body is not the only perspective available to our human brains. I am looking at my own death as an old man and more and more I find the human conditioning that has existed since history began is what is being discussed here; death is something to be afraid of. I also take issue with his quote that Buddha believed life was about suffering. Not what I am taking out of his readings. But back to the question at hand, can death help us live and I for one say Hell yes and not because of fear. To fear death is to fear growth. What I do appreciate about the article and this question is I believe humans would benefit from talking about death more often and taking the topic away from religions who have created the disillusionment in the first place.

There are two technical books he's published. One is Causality (2008) which is very technical and requires a fair amount of math background to understand and work through. He also has "Causal Inference in Statisrics: A Primer" which presents the core concepts with significantly less math pre-requisits

His do calculus is interesting, and he's highly influential in the machine learning literature, but he has a fair amount of detractors.

I personally like the concept that he presents in which we can reverse causality by re-ordering our equations. It points to the epistemological limits of our ability to understand causation in a way that Hume elucidated with his billiard ball examples a couple hundred years ago.

That said, Pearl is a bit arrogant for my taste, coming across as if he's the sole inventor of concepts that have existed for hundreds of years. His framework is a good one, but it is far from the only one.

Electric fence doesnt always have to be turned on, only once.

> The only way to infer causality is to reach into a system and modify it.

This seems, to me, to be an unfounded strong claim about inference that entails causality always being obliged to be empirical. Which, essentially, reduces econometrics, as it exists, to being entirely correlative knowledge because it is composed entirely of historical data.

What if there is no "cause knob" but, also, the set of data, C, at time 0 always results in the specific set of data, E, at time x>0, but a random set of data, Rn, at any time x where n<>x? There is nothing to modify since Modifying C changes the set and so there is no transition C->E. Which, essentially, means you have frustrated, prevented, blocked - essentially interrupted - the causal connection between C & E. This might not seem to be clearly expressed but it does actually require that causality is holistically considered: you have to take all the nodes and arcs of the graph into account.

You might say, that is simply a description of correlation and always was, and your claim might seem convincing. But how do you exclude causality. Even at a vanishingly small probability, the statement "that C causes E" is a fact; and a legitimate claim to make, even if you must qualify it by saying but only once in a billion. You might say one in a billion means it will never happen. Which is not a great claim. The probability of winning the lottery is, say, one in a billion - or tens of billions - yet there is more than one lottery winner since it started. The point being that, just because something has a low probability of happening does not forbid it happening.

> "when A changes, the B tends to change too" doesn't get you there (even if e.g. there's a time delay).

So, the idea here is not proven by your claims. You can infer causality by passively looking at data. Econometrics does it all the time. The deeper problem being that we live in a Universe that is deeply causal. Which suggests that starting from an assumption that there is "no causality involved" is a flawed premise. A flawed premise that is easily rejected because the data was created by a person not a random process and, therefore, you need good reason to reject the notion that the data "has" causality locked into it.

The idea of causality as being purely mechanistic, which is what it seems you are supposing here, is not the only way you can reason about causality.

Obviously you can infer causation from raw "passive" data. What else could our brains possibly be doing when they learn? We don't affect most things.

One way imagine how it's possible is to contrast the DAGs A->C, A->D, B->C, B->D, C->E, C->F, D->E, D->F and the one with arrows flipped. Then think about conditional dependence, P(C|D,A,B) = P(C|A,B) vs. P(C|D,E,F) != P(C|E,F). Knowing everything about effects can increase mutual information between C and D; knowing everything about causes can't. That's how you can distinguish between this DAG and the backwards one using only correlations. No need to intervene anywhere.

No no. That's so lazy. I like to at least put some thought into gifts that I give loved ones.

You don't have to assume the explanations are true for them to have utility

Put more simply certain mathematical assumptions required for causality cannot be justified from the data alone; it has to be argued.

Also "Causality", also by Pearl. More technical.

Making an appeal to the authority of the behavior of deontologists isn't persuasive. Most dietitians eat pizza. Pointing that out isn't evidence that pizza is health food.

Comprehensive theories that cannot be comprehensively articulated are not comprehensive theories. I.E. it is a real problem that we cannot say whyfor it is moral to drive to work with a risk of killing someone's at 0.0000001% and immoral at 5%.

I swept my floors today and regularly keep cooking utensils sterilized to aide my survivability. From my perspective, it's an improvement of my living conditions, not the human race as a whole or the quality of its gene pool.

Likewise I wouldn't expect education to directly alter the genetic material of a species. How is this even controversial?

Edit: but you could, through education, attempt to alter the mating strategies of a population, which could be rooted in eugenics.

You can only "solve" moral problems with logic or mathematics once you've already assumed a particular moral philosophy or ethical framework- consequentialism, for instance.

But which moral philosophy/ethical framework is correct or superior is the crucial question; once you have an ethical framework the solution to most moral dilemmas follows fairly straightforwardly, and in the case of utilitarianism/consequentialism may even boil down to no more than simple arithmetic... whereas in other moral frameworks (e.g. deontic systems) quantities are irrelevant and so mathematics has nothing to say.

So this blog's thesis isn't all that objectionable, so far as it goes, but it seems to me that its just that its addressing the least tricky or difficult aspect of moral reasoning and so isn't telling us anything particularly useful or anything which we didn't already know or tend to agree on.

Oh my god, people are experiments, and it's in our money and religions.

I completely agree that you can’t infer causality by “passively” looking at data, in the sense that it sounds like what you’re describing is naively looking at a scatter plot or running a regression of Y on X.

The key insight of causal econometrics is exactly the point you’re making, that in order to understand causality we have to somehow approximate the environment that is present in a lab, where we can randomly assign individuals to treatment and control groups, ensuring that people in both groups are on average the same and thus the only difference in expectation between these groups is the treatment of interest. Of course, we can’t do this with observational data, so we look for natural experiments, or environments where random distribution of treatment may occur among some population by chance.

There are a lot of specific methods, but the essence of them all is that, as long as there is some feature that is as-good-as randomly distributed between people, and that feature is correlated with the treatment we care about, we can use variations in that random factor to estimate the causal effect of changing treatment for those individuals who shift their behavior because of the random variable. An early example in economics is using variation in military participation driven by the Vietnam draft lottery to estimate the causal effect of military participation on lifetime earnings. So in that way, economists really do try to estimate causality by looking for situations in which we might think the “cause” knob is being turned due to historical or institutional quirks.

I’m just skimming the surface, but if you’re interested you should check out Mostly Harmless Econometrics by Angrist and Pischke (the former of whom won the Nobel Prize last year for these findings) or Causal Inference: The Mixtape. Our capability of being very confident about causality in data is definitely limited to when we can find these “natural experiments,” but researchers have been able to find quite a lot and it really forms the basis of modern empirical economics research.

Thanks!

Yeah I agree with you. Typically, when you get into academic research, the domain experts fully appreciate how complicated the situation is, and know how to properly interpret causal claims.

> Econometrics, the economic sub-discipline of statistics, is almost chiefly concerned with understanding when we can say that statistical estimates can be interpreted as causality

IMO (and this is controversial), you can never infer causality from looking passively at data--data alone can't discern between causation and correlation. It can only lend support to a working theory (i.e. if you already have a proposed causal mechanism).

The only way to infer causality is to reach into a system and modify it. If you can turn the "cause" knob and consistently observe the effect, you can infer causality. But passively peering in and seeing "when A changes, the B tends to change too" doesn't get you there (even if e.g. there's a time delay).

But I do think others would disagree with me.

Totally! However, the way I’ve seen it described, generally, focuses on the the topic and not the person saying it. For example, Black Holes: it is hard to be an expert in this area, but there are so many ways to look at the topic of black holes that any single way is both 1) inadequate, and 2) could be made to sound complete.

Totally agree! One metaphor I didn't include: a causal explanation is a lens for looking at a particular domain. It clarifies some things, but obscures others.

I'd never heard of IOED! Thanks for sharing. Sounds like it's related to Dunning-Kruger.

What I personally see is a lot of correlation mistaken for causality.

I’m getting my phd in economics with a focus on empirical estimation, and I want to offer some support for your article as well as some perspective from one of the disciplines you cited in the post. In general, I think statements of causality like the Brooks piece you cited are incredibly misleading and entirely unprovable, usually marred by reverse causality and the ommission of other potential explanatory variables. I totally agree that any causal statements, especially those made by political actors, should be viewed with skepticism.

But as far as causal claims within academic research, I do believe economics takes it more seriously than other non-quantitative disciplines. Econometrics, the economic sub-discipline of statistics, is almost chiefly concerned with understanding when we can say that statistical estimates can be interpreted as causality. In the last few decades, researchers have become even more precise in their understanding of what types of causality we’re measuring (I.e. what portion of the population it’s relevant to). In general, we’re considering the causal effects of policies or behavior rather than causation of events in history, which is, as you suggest, nearly impossible to parse in most scenarios. Our reach can certainly be limited and we don’t get it right 100% of the time, but every economist I know does not make causality claims lightly. Perhaps this reveals a bit of my personal bias for a discipline that I am fascinated by, and nothing you’re saying here directly implies that you disagree with anything I’ve written here, but I wanted to add a bit of my own perspective from someone who feels we can all be a bit too quick to state two things are connected by causality and spends most of their time trying to figure out when we can actually make those claims. Great article! :)