Structural Causal Models (SCMs) and Directed Acyclic Graphs (DAGs) are foundational tools for moving beyond pattern-finding into genuine causal understanding. Here's why they matter:

Distinguishing Causation from Correlation

Traditional statistics excels at finding associations, but associations can be misleading. SCMs and DAGs provide a formal language for encoding why variables relate — not just that they do. This is essential for any analysis where you want to intervene, predict under new conditions, or explain mechanisms.

Controlling for the Right Variables

DAGs reveal which variables to include (or exclude) in a model — a non-obvious problem. Three key structures illustrate this:

• Confounders (common causes of X and Y) — must be controlled to avoid spurious associations
• Colliders (common effects of X and Y) — must not be controlled, or you introduce bias
• Mediators (variables on the causal path from X to Y) — controlling for them blocks the effect you're trying to measure

Getting this wrong with purely statistical intuition is easy; DAGs make the logic explicit and checkable.

The Do-Calculus and Interventional Reasoning

Judea Pearl's do-calculus, built on SCMs, distinguishes between observing that a variable takes a value versus setting it (an intervention). This lets analysts answer questions like "what would happen if we forced X to change?" using observational data — under identifiable conditions — which is enormously valuable when experiments are costly or unethical.

Counterfactual Analysis

SCMs support counterfactual reasoning: "What would the outcome have been had the treatment been different, for this specific individual?" This underpins fairness analysis, policy evaluation, and personalized decision-making in ways that purely predictive models cannot.

Communicating Assumptions Transparently

Every causal analysis rests on assumptions. DAGs make those assumptions explicit and visible, allowing collaborators, reviewers, and domain experts to critique the model structure rather than hidden statistical choices. This improves scientific rigor and reproducibility.

Practical Applications

The value shows up across many fields: epidemiology (estimating treatment effects), economics (policy analysis), machine learning (building models robust to distribution shift), and AI fairness (detecting and correcting discriminatory pathways).

In short, SCMs and DAGs are important because they provide the scaffolding needed to answer causal questions rigorously — the questions that most actually matter for decision-making — rather than just describing data as it happens to be.

Short Answer: Not Reliably — But Usefully, With the Right Framing

What AI Can Do Reasonably Well

Encoding domain knowledge into DAG structure

If a human expert provides the causal assumptions (e.g., "X affects Y, Z is a confounder"), AI can translate these into a valid DAG and correctly apply algorithms like the backdoor criterion, frontdoor criterion, or do-calculus to identify valid adjustment sets. This is essentially symbolic reasoning over a graph, and LLMs can handle it competently for simple-to-moderate DAGs.

Algorithmic identification given a DAG

Given a well-specified DAG, tools like dagitty, bnlearn, or Python's causalgraphicalmodels are deterministically correct — and AI can reliably generate and interpret their outputs.

Where It Breaks Down

DAG construction from data alone is deeply unreliable

Causal structure cannot be identified from observational data without strong assumptions. Algorithms like PC, FCI, or GES recover Markov equivalence classes — sets of DAGs with the same conditional independencies — not unique causal graphs. AI has no principled way to choose among them without domain knowledge.

LLMs hallucinate causal structure

When asked to generate a DAG "from scratch" for a domain, LLMs tend to produce plausible-looking but unfounded graphs. They may:

• Confuse correlation with causation in their training data
• Omit latent confounders they have no way to know about
• Confidently assert edges that reflect statistical associations, not mechanisms

The identification problem is hard even with a correct DAG

Adjustment set identification requires checking graphical conditions (d-separation, admissibility) that become computationally and conceptually complex with:

• Hidden/latent variables
• Selection bias nodes
• Cycles or feedback loops
• Interference between units

No ground truth for validation

Unlike math or code, there's often no way to verify a generated DAG is correct without running an experiment — which defeats the purpose.

The Deeper Epistemological Issue

DAGs are representations of assumptions, not discoveries. A DAG is only as valid as the domain knowledge baked into it. The question "did AI get the DAG right?" is often unanswerable without a randomized experiment or near-experimental variation. This makes AI-generated DAGs particularly dangerous when used naively — they provide false confidence in causal conclusions drawn from observational data.

Practical Verdict

The most defensible workflow is: human provides causal assumptions → AI encodes them as a DAG → formal algorithm identifies adjustment sets → human audits the assumptions. AI as a reasoning assistant over explicit assumptions is sound; AI as a causal discoverer is not.

For all but the most trivial of scientific questions, rigorous analysis in research will require conditioning, such as stratification or adjustment. Practically, all observational data analyses and even nearly all RCTs require analyses involving conditioning and adjustment. Almost immediately, statistical modeling offers several advantages in conditioning and adjustment: for efficiency with limited information, for using continuous covariates with minimal information loss, for managing complexity (bias, effect modification, missing-ness, heteroskedasticity, etc.) in an integrated and coherent framework; and now with modern tools, advantages for visualization of information and patterns in the data.

The regression modeling framework for statistical analysis accommodates the all the fundamental statistical interests of (i) estimation, (ii) hypothesis testing, (iii) description, and (iv) prediction. Even simple univariate and bivariate analyses are special cases of more general regression problems that make strong assumptions about ignorability of covariates and which arbitrarily set coefficients for these covariates to zero. There are special advantages for approaching regression problems from a prediction perspective: if you get the left hand-side correct (Ŷ) you get better performance for estimation, hypothesis testing and even description. Therefore, methods for optimizing prediction confer advantages and benefits for other statistical and research objectives. But doing prediction well is as much craft as it is technical, and receiving the experience and insight necessary for cultivating this craft is an important factor for effectiveness and success in scientific work. The wisdom—often non-obvious or even counter-intuitive–cataloged in the Regression Modeling Strategies (RMS) text, ebook, and the short course are a unique and rare opportunity to rapidly develop statistical acumen and accelerate scientific sophistication. RMS is also a gateway and stepping stone for other modern statistical applications such as Bayesian methods and hierarchical models, robust statistical techniques, biomarker identification, adaptive trials, comparative effectiveness, meta-analysis and evidence synthesis, etc.; and for judicious use of machine learning/AI, etc.

RMS helps us better understand and manage sources of uncertainty in scientific work; thus, RMS makes us all better scientists, and better—more sophisticated—consumers and administrators of research.