“Cause” is Mechanistic Narrative within Scientific Domains: An Ordinary Language Philosophical Critique of “Causal Machine Learning”

Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Next we'll be talking about the paper "“Cause” is Mechanistic Narrative within Scientific Domains: An Ordinary Language Philosophical Critique of “Causal Machine Learning”".

Jane: The paper was written by Vyacheslav Kungurtsev, Leonardo Christov Moore, Gustav Šír and Martin Krutský from Czech Technical University in Prague and Institute of Advanced Consciousness Studies.

Tom: Stay tuned as we take you through the paper and discuss its implications.

Title: Tom: Welcome back to the show, everyone. Today we're looking at a paper that's going to make a lot of people in machine learning squirm a little bit. It's called “Cause” is Mechanistic Narrative within Scientific Domains: An Ordinary Language Philosophical Critique of “Causal Machine Learning.”

Jane: And Tom, I have to say, just reading that title out loud makes me want to take a deep breath. It's a mouthful, but it's also a direct challenge to a whole field that's been booming for years.

Tom: Exactly. The paper is basically saying that when we talk about "causal machine learning," we might be overpromising what the math can actually deliver. The authors come from computer science and philosophy, and they're asking a really basic question: what do we actually mean when we say one thing causes another?

Jane: And the answer, according to them, isn't a universal law. It's different in physics, different in biology, different in economics. The word "cause" does different work in each of those fields.

Tom: Right, and that's the "ordinary language" part of the title. That's a philosophical tradition that says we should look at how people actually use words in real contexts, not try to define them in the abstract.

Jane: So instead of saying "here's the one true definition of causality," they're saying "let's look at how a physicist uses the word, how a doctor uses it, how an economist uses it." And surprise, surprise, they use it differently.

Tom: And that's a problem for the machine learning folks who claim their algorithms are discovering "true" causal relationships, because the algorithm is using one specific mathematical definition and applying it everywhere.

Jane: It's like using a hammer on every problem and being surprised when the screws don't go in straight.

Tom: That's a good way to put it. The authors aren't saying the math is wrong, they're saying the claims about what the math proves are too strong.

Jane: And that's going to ruffle some feathers, because a lot of research funding and a lot of careers are built on the idea that causal machine learning is the next big thing.

Tom: Definitely. But before we get into the nitty-gritty of their argument, let's talk about who wrote this thing. The first author is Vyacheslav Kungurtsev, who's at Czech Technical University, and he's joined by researchers from the Institute of Advanced Consciousness Studies in LA.

Jane: So you've got a computer scientist and people who study consciousness and phenomenology. That's an interesting mix.

Tom: It is, and it shows in the paper. They're not just doing a technical critique, they're bringing in Wittgenstein, Quine, Kuhn, all the big names in philosophy of science.

Jane: So this isn't a paper that's going to give you a new algorithm. It's a paper that's going to make you question whether the algorithms you already have are doing what you think they're doing.

Tom: And that's exactly why we wanted to talk about it. So stick around, because next we're going to get into the actual argument they make about how causality works in different scientific fields.

Jane: And trust me, it gets interesting when they start talking about physics versus biology versus the social sciences.

Summary: Tom: So we're back, and we're still talking about “Cause” is Mechanistic Narrative within Scientific Domains. Jane, you were going to walk us through the core argument.

Jane: Right. So the paper starts by looking at how causal machine learning actually works. It's based on these things called Bayesian networks, which are basically graphs where each node is a variable and each arrow means "this variable influences that one."

Tom: And the algorithm tries to learn the structure of that graph from data, using statistical tests for conditional independence.

Jane: Exactly. And the key assumption is something called "causal sufficiency," which means you've measured all the common causes of your variables. Nothing hidden is messing things up.

Tom: And that assumption, as the paper points out, almost never holds in the real world. Especially in biology or social science, there are always hidden confounders.

Jane: Right. But the bigger issue is more philosophical. The paper argues that the very idea of "cause" is tied to the specific scientific domain you're working in. In physics, cause is embedded in differential equations. The equations don't just describe what happens, they define the mechanism.

Tom: So when a physicist says "gravity caused the apple to fall," that's shorthand for a whole system of equations that fully describes the situation.

Jane: And in that context, causal machine learning can actually work, because the math matches the physics. The graph structure can correspond to the actual forces at play.

Tom: But then you move to biology, and things get messy. The paper calls biological systems "open and irreducible." You've got multiple scales, from molecules to cells to organs to whole organisms, and causes at one scale don't cleanly map to causes at another.

Jane: And in the social sciences, it's even worse. Human behavior is so complex that no set of equations can capture it. So what do researchers do? They tell stories. They create narratives that make sense of the data.

Tom: And that's where the title comes in. The paper says "cause" is really a mechanistic narrative within a scientific domain. It's a story that connects inputs to outputs in a way that makes sense to the experts in that field.

Jane: So the smoking and lung cancer example is perfect here. It wasn't one study that proved it. It was decades of research across epidemiology, biochemistry, physiology, all pointing in the same direction.

Tom: And each field contributed its own piece of the causal story. The epidemiologists showed the statistical association, the biochemists showed the mechanism, the pathologists showed the tissue damage.

Jane: And only when all those pieces fit together did the scientific community say "yes, smoking causes cancer." That's the preponderance of evidence approach.

Tom: So the paper's critique is that causal machine learning tries to skip all that. It tries to jump straight from data to causal claims without the mechanistic understanding.

Jane: And that's why the authors say we should be much more careful about the language we use. Instead of saying "this algorithm discovered that A causes B," we should say "this algorithm found a statistical pattern consistent with A influencing B."

Tom: It's a humility thing. And it's going to be controversial, because a lot of people in the field really do believe they're finding true causes.

Jane: They might be, sometimes. But the paper's point is that you can't know that from the algorithm alone. You need the domain expertise, the mechanistic understanding, the whole narrative.

Tom: So what's the fix? What are the authors actually suggesting we do differently? That's what we're going to dig into next.

Improvements: Tom: Okay, so we've established the problem. Causal machine learning overclaims. But what do the authors actually want us to do about it? That's what we're digging into now.

Jane: And I think the most practical suggestion is about language. They want researchers to stop saying "we discovered a cause" and start saying "we found evidence consistent with a causal relationship."

Tom: It sounds like a small change, but it's actually huge. Because the way we talk about results shapes how other people use them.

Jane: Exactly. And they tie this to something called "epistemic virtue," which is basically the discipline of being honest about how much you actually know.

Tom: And that's not just for the researchers. It's for the journals, the press releases, the headlines. The whole chain of communication.

Jane: But they also suggest a more positive direction. Instead of trying to replace domain expertise with algorithms, they want to see more integration. Mixed methods research that combines statistical analysis with mechanistic understanding.

Tom: So in medicine, that would mean a clinical trial plus a biochemical mechanism plus a physiological model, all working together.

Jane: Right. And they point to cognitive science as a great example of this. It's a field that brings together psychology, neuroscience, linguistics, philosophy, computer science, all studying the same phenomena from different angles.

Tom: And each angle contributes its own piece of the causal story. The neuroscientist talks about brain activation, the psychologist talks about behavior, the philosopher talks about consciousness.

Jane: And none of them has the whole picture, but together they build a richer understanding than any single field could achieve.

Tom: The paper also talks about how neuro-symbolic AI and explainable AI could help with this. Instead of just giving you a prediction, the model could give you a narrative that connects the inputs to the outputs in a way that makes sense to a domain expert.

Jane: So the AI becomes a tool for generating hypotheses, not for delivering final answers. It's a partner in the scientific process, not a replacement for it.

Tom: And that's actually a much more realistic and useful role for AI in science. Because the really hard problems, the ones that matter for human health and society, they're not going to be solved by a single algorithm.

Jane: They're going to be solved by teams of people from different fields, using different tools, and the AI can help them talk to each other.

Tom: There's also a really interesting point about clinical intuition. The paper says that practitioners who work with a system every day often have knowledge that isn't captured in any formal model.

Jane: And that knowledge should be respected, not dismissed because it doesn't fit into a statistical framework.

Tom: So the improvements here are about humility, integration, and respect for different kinds of knowledge. It's a much more collaborative vision of science.

Jane: And it's a vision where AI is a powerful tool, but not an oracle. That's a much healthier relationship.

Tom: So let's wrap this up. We've talked about the critique and the suggestions. What's the big picture here? That's coming up in our final segment.

Conclusion: Tom: Alright, we're at the end of our discussion of “Cause” is Mechanistic Narrative within Scientific Domains: An Ordinary Language Philosophical Critique of “Causal Machine Learning.” Let's pull it all together.

Jane: So the paper's core message is that causality isn't a single thing you can discover with one algorithm. It's a concept that takes on different meanings in different scientific contexts.

Tom: And the danger is when we take a tool designed for one context and apply it to another without thinking about whether the assumptions hold.

Jane: The authors aren't saying causal machine learning is useless. They're saying it's being oversold. The methods are powerful, but they don't deliver what they promise.

Tom: And the fix is to be more careful with our language, more humble about our claims, and more willing to combine statistical tools with deep domain expertise.

Jane: It's a call for a more mature, more integrated approach to science. One where AI is a collaborator, not a replacement for human understanding.

Tom: And honestly, that's a message that applies beyond just causal machine learning. It's about how we think about AI in general.

Jane: Yeah, because the temptation to overclaim is everywhere. It's not just in causal inference, it's in all of AI research.

Tom: So we should be grateful to the authors for reminding us to slow down and think about what we're actually saying when we make claims about what our models can do.

Jane: And to remember that the word "cause" carries a lot of weight. It's not just a statistical relationship, it's a statement about how the world works.

Tom: And that statement deserves to be backed up by more than just a p-value.

Jane: Exactly. So that's our take on this paper. It's a challenging read, but a valuable one for anyone working in machine learning, statistics, or any field that uses those tools.

Tom: And with that, we're going to say goodbye to this paper and get ready for the next one. Thanks for listening, everyone.

Jane: See you next time.