What that means is that the question of causality comes down to comparing actual outcomes with counterfactual outcomes. Causal inference methods employ various assumptions to let us estimate the unobservable counterfactual outcome. By doing this, we can use them to make the appropriate comparison and estimate the treatment effect.
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counterfactual prediction
To identify confounders, the question to ask is whether there are any variables that are not constant across the two groups of the population. If the answer is yes, confounders might be a problem.
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The challenges lie in how to find and control for confounders.
In statistics, econometrics, epidemiology and related disciplines, the method of instrumental variables (IV) is used to estimate causal relationships when controlled experiments are not feasible or when a treatment is not successfully delivered to every unit in a randomized experiment.
Historically, many machine learning approaches focus on predicting outcomes, not understanding causality, while many traditional causal inference approaches have faced challenges from high-dimensional datasets and complex environments in the absence of RCT.
I am attending this workshop today to learn more about applying causal inference to actual problems: https://datasciences.utoronto.ca/forging-a-path-causal-inference-and-data-science-for-improved-policy/
Creative questions:
How much did I understand based on the knowledge I have? I want to know the boundary of my knowledge.
AN:
I should measure the treatment effect of the ROI seller list.
Gang Fang's Blog 
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