Contents
What is one of the implication of the fundamental problem of causal inference?
The fundamental problem for causal inference is that, for any individual unit, we can observe only one of Y(1) or Y(0), as indicated by W; that is, we observe the value of the potential outcome under only one of the possible treatments, namely the treatment actually assigned, and the potential outcome under the other …
What is the fundamental problem of inference?
The Fundamental Problem of Statistical Inference (FPSI) states that, even if we have an estimator E that identifies TT in the population, we cannot observe E because we only have access to a finite sample of the population.
What are the two main inference problems?
When trying to estimate the effect of a program on an outcome, we face two very important and difficult problems: the Fundamental Problem of Causal Inference (FPCI) and the Fundamental Problem of Statistical Inference (FPSI).
How is causal inference used in data analysis?
The book describes various data analysis approaches that can be used to estimate the causal effect of interest under a particular set of assumptions when data are collected on each individual in a population. A key message of the book is that causal inference cannot be reduced to a collection of recipes for data analysis.
How is causal inference a complex scientific task?
Causal inference is a complex scientific task that relies on triangulating evidence from multiple sources and on the application of a variety of methodological approaches. No book can possibly provide a comprehensive description
Why is it hard to answer the causal question?
For example, it is not uncommon to find studies in which the effect estimates are hard to interpret because the data analysis methods cannot appropriately answer the causal question (were it explicitly stated) under the investigators’ assumptions (were they declared).