(First-order) causality actually isn't hard to determine in environments where first-order effects dominate. The way to determine causality here is through a combination of physical laws and controlled experimentation [1]. In fact, we have plenty of causal models (e.g. 1st principles physics-based models, or design-of-experiments models). Without these models, machines/control systems/etc would not work.

The trouble is, outside of these 1st-order effect dominant, deterministic environments, causality becomes much harder. In complex systems, stochasticity, nonlinearity, feedback loops and higher-order effects dominate. There's also emergent behavior -- properties that are true in the small are not true in the large.

Consider a complex system like human society -- can we truly determine causality of broad interventions? Likely not in a first-order way like in the physical sciences. We can do it imperfectly through tools like causal inference (Rubin) which makes much more modest claims about the "strength" of effects (average causal effect). Randomized Controlled Tests (RCT) is another tool for making causal claims.

But in a complex world, 2nd, 3rd and higher order effects dominate and so the notion of root causes itself becomes ambiguous. Richard I. Cook once said "post-accident attribution to a 'root cause' is fundamentally wrong". Though humans are attracted to the idea of a chain of simple causes (which is why we have the myth of Mrs O'Leary's cow kick over a lantern and starting the Great Chicago Fire of 1871), there's typically no easily-identified root cause. First-order causal thinking assumes a Directed-Acyclic-Graph (DAG) idea of a causality chain which converge into a set of effects, but the reality is that such a DAG, if it can be represented, is likely to be infinitely complex in a complex environment. First-order causal thinking is an insufficient mental model in a complex environments.

Instead, I think instead of aiming for a deep understanding of epistemic causality (where we try to know and represent causality), it's probably more useful to focus on instrumental causality (where we aim to know the main points of leverage that are effective in changing the system). I think we'll likely get very far just by finding the knobs that have the most effect on the variables we would like to change (that don't also simultaneously change variables that we wouldn't want to change).

[1] to determine causality, we typically have to perturb the system -- determining causality through observational data is possible, e.g. via natural experiments, but there are many epistemic restrictions which limit the claims that can be made.