Many commonly used treatment effects estimators rely on the unconfoundedness assumption ("selection on observables") which is fundamentally non-testable. When evaluating the effects of labor market policies, researchers need to observe variables that affect both treatment participation and labor...

Many commonly used treatment effects estimators rely on the unconfoundedness assumption ("selection on observables") which is fundamentally non-testable. When evaluating the effects of labor market policies, researchers need to observe variables that affect both treatment participation and labor...

A large and highly used number of treatment effects estimators rely on the unconfoundedness assumption ("selection on observables") which is fundamentally non testable. When evaluating the effects of labor market policies, researchers need to observe both variables that affect treatment...

Many countries support business start-ups to spur economic growth and reduce unemployment with different programmes. Evaluation studies of such programmes commonly rely on the conditional independence assumption (CIA), allowing a causal interpretation of the results only if all relevant...

Many countries support business start-ups to spur economic growth and reduce unemployment with different programmes. Evaluation studies of such programmes commonly rely on the conditional independence assumption (CIA), allowing a causal interpretation of the results only if all relevant...

Many countries support business start-ups to spur economic growth and reduce unemployment with different programmes. Evaluation studies of such programmes commonly rely on the conditional independence assumption (CIA), allowing a causal interpretation of the results only if all relevant...

Many countries support business start-ups to spur economic growth and reduce unemployment with different programmes. Evaluation studies of such programmes commonly rely on the conditional independence assumption (CIA), allowing a causal interpretation of the results only if all relevant...

Many commonly used treatment effects estimators rely on the unconfoundedness assumption ("selection on observables") which is fundamentally non-testable. When evaluating the effects of labor market policies, researchers need to observe variables that affect both treatment participation and labor...

A large and highly used number of treatment effects estimators rely on the unconfoundedness assumption ("selection on observables") which is fundamentally non testable. When evaluating the effects of labor market policies, researchers need to observe variables that affect both treatment...