What is the Unconfoundedness assumption?
The unconfoundedness assumption says loosely that all the variables affecting both the treatment T and the outcome Y are observed (we call them covariates) and can be controlled for.
What is unconfoundedness?
Unconfoundedness, a term coined by Rubin (1990), refers to the case where (non-parametrically) adjusting for differences in a fixed set of covariates removes biases in comparisons between treated and control units, thus allowing for a causal interpretation of those adjusted differences.
What is ignorability in causal inference?
By randomly assigning treatment, researchers can ensure that the potential outcomes are independent of treatment assignment, so that the average difference in outcomes between the two groups can only be attributable to treatment. This assumption is formally called ignorability.
What is ignorability assumption?
The ignorability assumption means that if we want to interpret the regression coefficient for treatment as an average causal effect then all the counfounding covariates should be controlled for in the regression model. This is why we can you use the difference in means to estimate the causal effects.
What is the common support assumption?
This assumption of common support ensures that there is sufficient overlap in the characteristics of treated and untreated units to find adequate matches. When these assumptions are satisfied, the treatment assignment is said to be strongly ignorable in the terminology of Rosenbaum and Rubin (Biometrika, 1983).
What is the common support?
Common support is subjectively assessed by examining a graph of propensity scores across treatment and comparison groups (Figure 1). A starting test of balance is to ensure that the mean propensity score is equivalent in the treatment and comparison groups within each of the five quintiles (Imbens 2004).