A field fixes itself: the applied turn in economics

Definitely better than what came before and surely in another 30 years empirical standards will be even higher!

2. causal discovery methods + generic assumptions

Economists are really putting effort into sound methodology.

Taking the experiment as an ideal we cannot reach, there are many settings where there is useful variation in the data which can approximate the random assignment to treatment which is the essence of experiments.

Some canonical research designs which can be used here include differences in differences, regression discontinuity, synthetic control methods, or instrumental variables methods. They are differently appropriate to different settings but permit causal claims to be made. These methods are widely used in contemporary empirical economics (indeed this is a nearly exhaustive list of methods used in applied micro!).

Useful, accessible references here include Angrist and Pischke, “mostly harmless econométrics,” or “causal inference the mixtape” by a guy at Baylor whose name I’m forgetting, and the very recent “the effect” by Huntington-Klein, which I have not yet read.

Other slightly more exotic models are used in (for example) industrial organization which still permit causal claims to be made about the effects of increasing prices or changing product features on demand for a product or set of products.

What causal inference does is estimate the parameters of a given causal model, and does so by applying algorithms on the model to determine which variables to control for. Once the researcher to supply directions of causality, you can use the data to validate the model, or ask questions about it that you already know the answer to as a cross check. But there can be multiple valid models, and the data alone cannot help you select the "right" one. Worse, sometimes (often?) validating the "right" model requires data you do not have access to. But you don't need to do the experiment to find that out at least!

Underlying all this appears to be Bayesian methods. Which makes sense as you are often seeking answers to conditional probabilities, Bayes is good for that.

The strange thing is, even with "measurements" like CO2/global warming so clearly pointing to armageddon in a few generations, the field of economics is STILL unable to quantify such impacts on models, or produce effective economic pricing measures.

Of course, because in addition to being only a crude "science" that basically reduces to bad accounting practice on a massive scale, economics is also dressed up as a "crystal ball science" that the oligarchical powers can trot out to justify their positions and current structure of monetary distribution as a priori destiny.

So it is ALSO politically corrupt, not just computationally bankrupt.

The reality is of course that there is plenty of money to deal with most of our massive structural problems. But who owns that money? The rich who will rather see the world destroyed than give it up.

Building tractable mathematical models. Explain phenomenons. Empirical results are used to validate these days.

A classic model is supply & demand curves -- as price increases supply increases, demand falls, with certain exceptions. Empiricists have spent significant effort doing RCTs to demonstrate the model works and there are exceptions. The model stands tall even today.