[Mediation-information] Trouble with mediate.ped function in Mediation for R

[Kosuke Imai]

You can also do a modeling approach based on two-stage least squares.  In
your case, this might be the easiest.  Take a look at page at the end of
page 783 and the beginning of page 784 of this paper for discussion:
http://imai.princeton.edu/research/files/mediationP.pdf

Kosuke Imai
Professor, Department of Politics
Center for Statistics and Machine Learning
Princeton University
http://imai.princeton.edu

On Sat, Aug 12, 2017 at 1:38 AM, Teppei Yamamoto <teppei at mit.edu> wrote:

> Hi Greg,
>
> The function can produce those values for the bounds when your data are
> not consistent with the identification assumptions, such as the consistency
> assumption or the exclusion restrictions. It could also be due to some
> mistake in the data (e.g. miscoding in a variable). If you send me your
> code and the dataset (maybe a subset that reproduces the same problem, if
> you cannot send the whole thing), I'll be happy to look into it to the
> extent I can.
>
> We unfortunately don't have a plan to extend it to a continuous outcome
> variable, primarily because our theoretical framework doesn't simply
> generalize to such a setup. There are alternative approaches that could
> potentially achieve what we need, though, so we (or someone else) might
> someday get to it -- not on our immediate agenda unfortunately!
>
> Best,
> Teppei
>
> ------------------------------------------
> Teppei Yamamoto
>
> Associate Professor of Political Science
> Alfred Henry and Jean Morrison Hayes Chair
> Massachusetts Institute of Technology
>
> http://web.mit.edu/teppei/www/
> ------------------------------------------
>
>
> On 8/11/17 08:17, GREG PORUMBESCU wrote:
>
>> Dear All,
>>
>> My colleagues and I are trying to analyze data coming from a parallel
>> encouragement design using the mediate.ped function in the mediate R
>> package. Our encouragement, time, has three levels: 1 (long time), -1
>> (short time) and 0 (no time limit). We have two questions:
>>
>> 1) We have followed the instructions provided in Tingley et al. 2014, but
>> when we run the syntax, the lower and upper bound confidence intervals on
>> all of the ACME report 0. These estimates hold even when we used different
>> (binary) mediators. The code we are using and the output is as follows:
>>
>>  > ped<- mediate.ped("PERFORMANCE_binary", "sum_obund_new_binary",
>> "negframe", "time", DFC_coded)
>>  > summary(ped)
>>
>> Design-Based Causal Mediation Analysis
>>
>> Parallel Encouragement Design
>>
>>                            Lower Bound Upper Bound
>> Population ACME (control)           0           0
>> Complier ACME (control)             0           0
>> Population ACME (treated)           0           0
>> Complier ACME (treated)             0           0
>>
>> Sample Size Used:  610
>>
>>
>> Would you have any idea what is causing our estimates to behave this way?
>> Any advice on how to resolve this issue?
>>
>> 2) In our code, we transform our outcome variable, PERFORMANCE, into a
>> binary variable in order to run mediate.ped, as the instructions indicate.
>> However, is there any way to run mediate.ped with a continuous outcome
>> variable? If not, are there any plans to allow for this in the future?
>>
>> Many thanks,
>>
>> Greg
>>
>> Best wishes,
>>
>> Gregory A. Porumbescu
>> Assistant Professor
>> School of Public Affairs and Administration
>> Rutgers University Newark
>> https://spaa.newark.rutgers.edu/gregory-porumbescu
>>
>> https://scholar.google.com/scholar?hl=en&q=gregory+porumbesc
>> u&btnG=&as_sdt=1%2C14&as_sdtp=&oq=gre
>>
>>
>>
>> ᐧ
>>
>>
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