Treatment analyses based on average outcomes do not immediately generalize to the case of ordered responses because the expectation of an ordinally measured variable does not exist. The proposed remedy in this paper is a shift in focus to distributional effects. Assuming a threshold crossing model on both the ordered potential outcomes and the binary treatment variable, and leaving the distribution of error terms and functional forms unspecified, the paper discusses how the treatment effects can be bounded. The construction of bounds is illustrated in a simulated data exampl
This paper considers the identification of treatment effects on conditional transition probabilities...
This paper develops a nonparametric model that represents how sequences of outcomes and treatment ch...
In this paper, we explore partial identification and inference for the quantile of treatment effects...
This paper deals with the identification of treatment effects when the outcome variable is ordered. ...
In this paper, we study partial identification of the distribution of treatment effects of a binary ...
This paper discusses how to identify individual-specific causal effects of an ordered discrete endog...
In the presence of an endogenous binary treatment and a valid binary instru- ment, causal effects a...
This paper considers the evaluation of the average treatment effect of a binary endogenous regressor...
This paper considers the evaluation of the average treatment effect of a binary endogenous regressor...
We present a new command, tebounds, that implements a variety of techniques to bound the average tre...
This paper discusses how to identify individual-specific causal effects of an ordered discrete endog...
Abstract—Recently there has been a surge in econometric work focusing on estimating average treatmen...
This paper discusses how to identify individual-specific causal effects of an ordered discrete endo...
Researchers using instrumental variables to investigate the effects of ordered treatments (e.g., yea...
Variability in individual causal effects, treatment effect heterogeneity (TEH), is important to the ...
This paper considers the identification of treatment effects on conditional transition probabilities...
This paper develops a nonparametric model that represents how sequences of outcomes and treatment ch...
In this paper, we explore partial identification and inference for the quantile of treatment effects...
This paper deals with the identification of treatment effects when the outcome variable is ordered. ...
In this paper, we study partial identification of the distribution of treatment effects of a binary ...
This paper discusses how to identify individual-specific causal effects of an ordered discrete endog...
In the presence of an endogenous binary treatment and a valid binary instru- ment, causal effects a...
This paper considers the evaluation of the average treatment effect of a binary endogenous regressor...
This paper considers the evaluation of the average treatment effect of a binary endogenous regressor...
We present a new command, tebounds, that implements a variety of techniques to bound the average tre...
This paper discusses how to identify individual-specific causal effects of an ordered discrete endog...
Abstract—Recently there has been a surge in econometric work focusing on estimating average treatmen...
This paper discusses how to identify individual-specific causal effects of an ordered discrete endo...
Researchers using instrumental variables to investigate the effects of ordered treatments (e.g., yea...
Variability in individual causal effects, treatment effect heterogeneity (TEH), is important to the ...
This paper considers the identification of treatment effects on conditional transition probabilities...
This paper develops a nonparametric model that represents how sequences of outcomes and treatment ch...
In this paper, we explore partial identification and inference for the quantile of treatment effects...