3 ms·

What's the point of dumping a bunch of Google results here? At least half the results are about implementations of pretty traditional etatistical / econometric

by em500 7y ago

What's the point of dumping a bunch of Google results here? At least half the results are about implementations of pretty traditional etatistical / econometric inference techniques. The Rudin causal inference framework requires either randomized controlled trials or for propensity score models an essentially unverifiable separate model step. Google's CausalImpact model, despite having been featured on Google's AI blog, is a statistical/econometric model (essentially the same as https://www.jstor.org/stable/2981553 https://www.jstor.org/stable/2981553). It leaves it up to the user to find and designate a set of control variables, which has to be designated by the user to be unaffected by the treatment. This is not done algorithmically, and has very little to do with RNNs, Random Forests or regression regularization. > If you were to write a pseudocode algorithm for an econometric researcher's process of causal inference (and also their cognitive processes (as executed in a NN with a topology)), how would that read? [1] Set up a proper RCT, that is randomly assign the treatment to different subjects [2] Calculate the outcome diffences between the treated and untreated For A/B testing your website, the work division between [1] and [2] might be 50-50, or at least at similar order of magnitudes. For the questions that academic economists wrstle with, say, estimate the effect of increasing school funding / decreasing class size, the effect of shifts between tax deductions vs tax credits vs changing tax rates or bands, or of the different outcome on GDP growth and unemployment of monetary vs fiscal expansion [1] would be 99.9999% of the work, or completely impossible. Faced with the impracticallity/impossiblility of proper experiments, academic micro-economists have typically resorted to Instrumental Variable regressions. AFAICT finding (or rather, convincing the audience that you have) a proper instrument is not very amendable to automation or data mining. In academic macro-economics (and hence at Serious Institutions such as central banks and the IMF), the most popular approaches to building causal models in the last 3 or 4 decades have probably been 1) making a bunch of unrealistic assumpsions of the behaviour individual agents (microfoundations/DSGE models) 2) making a bunch of uninterpretable and unverifyable technical assumptions on the parameters in a generic dynamic stochastic vector process fitted to macro-aggregates (Structural VAR with "identifying restrictions") 3) manually grouping different events in different countries from different periods in history as "similar enough" to support your pet theory: lowering interest rates can lead to a) high inflation, high unemployment (USA 1970s), b) high inflation, low unemployment (Japan 1970s), b) low inflation, high unemployment (EU 2010s) c) low inflation, low unemployment (USA, Japan past 2010s) I really don't see how a RL would help with any of this. Care to come up with something concrete?

westurner 7y ago
> What's the point of dumping a bunch of Google results here? At least half the results are about implementations of pretty traditional etatistical / econometric inference techniques. Here are some tools for causal inference (and a process for finding projects to contribute to instead of arguing about insufficiency of AI/ML for our very special problem domain here). At least one AGI implementation doesn't need to do causal inference in order to predict the outcomes of actions in a noisy field. Weather forecasting models don't / don't need to do causal inference. > A/B testing Is multi-armed bandit feasible for the domain? Or, in practice, are there too many concurrent changes in variables to have any sort of a controlled experiment. Then, aren't you trying to do causal inference with mostly observational data. > I really don't see how a RL would help with any of this. Care to come up with something concrete? The practice of developing models and continuing on with them when they seem to fit and citations or impact reinforce is very much entirely an exercise in RL. This is a control system with a feedback loop. A "Cybernetic system". It's not unique. It's not too hard for symbolic or neural AI/ML. Stronger AI can or could do [causal] inference.

by em500 7y ago

westurner 7y ago

> What's the point of dumping a bunch of Google results here? At least half the results are about implementations of pretty traditional etatistical / econometric inference techniques. Here are some tools for causal inference (and a process for finding projects to contribute to instead of arguing about insufficiency of AI/ML for our very special problem domain here). At least one AGI implementation doesn't need to do causal inference in order to predict the outcomes of actions in a noisy field. Weather forecasting models don't / don't need to do causal inference. > A/B testing Is multi-armed bandit feasible for the domain? Or, in practice, are there too many concurrent changes in variables to have any sort of a controlled experiment. Then, aren't you trying to do causal inference with mostly observational data. > I really don't see how a RL would help with any of this. Care to come up with something concrete? The practice of developing models and continuing on with them when they seem to fit and citations or impact reinforce is very much entirely an exercise in RL. This is a control system with a feedback loop. A "Cybernetic system". It's not unique. It's not too hard for symbolic or neural AI/ML. Stronger AI can or could do [causal] inference.