<p>Here's the question that I asked to ChatGPT.</p>
<p>Suppose that I have an unbalanced panel data set and trying to use a differences-in-differences approach to estimate the average treatment effect of a certain policy. Would including individuals that only have the pre-treatment observations(or alternatively, having only the post-treatment observations) bias the estimation?</p>
<p>This is the answer that I received.</p>
<pre><code>In a differences-in-differences (DiD) approach, including individuals who only have pre-treatment observations or only post-treatment observations can introduce bias into the estimation of the average treatment effect (ATE) of a policy. Here's why this can be problematic and how to handle it:

Potential Issues with Including Individuals with Incomplete Observations
Lack of Within-Individual Variation:
The DiD method relies on comparing changes over time between a treatment group and a control group. Individuals with only pre-treatment or only post-treatment observations do not provide any within-individual variation to estimate the change due to treatment. Including them could distort the estimation because their effect on the dependent variable cannot be properly measured relative to the treatment.
Different Characteristics:
Individuals observed only in one period (pre- or post-treatment) may have different characteristics or unobserved factors compared to those observed in both periods. If these differences correlate with the outcome, it can lead to biased estimates.
Changes in Population Composition:
The composition of the population might change from pre- to post-treatment periods. Including individuals who only appear in one of the periods can introduce bias if their entry or exit from the dataset is related to the treatment or the outcome variable.```


Now for convenience, say that whether an individual has both the data of pre and post treatment, or has partial(i.e. only pre or only post) is determined totally randomly. Would it still be problematic to include individuals with only partial data?
</code></pre>

CC BY-SA 4.0
2024-08-15T05:45:53.370Z
It will not necessarily do so but it can. The short answer is that it depends. In order to get a better answer you have to ask a more well designed question. A very important issue is why the missingness occurs. If the missingness is systematic then your estimate of what the treated units would be like had they not been treated could very well be biased and this would result in bias of treatment effect.
Jesper Hybel
CC BY-SA 4.0
2024-08-15T08:00:31.890Z
@JesperHybel consider maybe expanding the comment into an answer
1muflon1
<difference-in-difference>
Can observations with only pre-treatment period data influence Diff-in-Diff results?
Wooyong Park
# Can observations with only pre-treatment period data influence Diff-in-Diff results?

(Asked by: username_0 on 2024-08-15)

Here's the question that I asked to ChatGPT.
Suppose that I have an unbalanced panel data set and trying to use a differences-in-differences approach to estimate the average treatment effect of a certain policy. Would including individuals that only have the pre-treatment observations(or alternatively, having only the post-treatment observations) bias the estimation?
This is the answer that I received.
In a differences-in-differences (DiD) approach, including individuals who only have pre-treatment observations or only post-treatment observations can introduce bias into the estimation of the average treatment effect (ATE) of a policy. Here's why this can be problematic and how to handle it:

Potential Issues with Including Individuals with Incomplete Observations
Lack of Within-Individual Variation:
The DiD method relies on comparing changes over time between a treatment group and a control group. Individuals with only pre-treatment or only post-treatment observations do not provide any within-individual variation to estimate the change due to treatment. Including them could distort the estimation because their effect on the dependent variable cannot be properly measured relative to the treatment.
Different Characteristics:
Individuals observed only in one period (pre- or post-treatment) may have different characteristics or unobserved factors compared to those observed in both periods. If these differences correlate with the outcome, it can lead to biased estimates.
Changes in Population Composition:
The composition of the population might change from pre- to post-treatment periods. Including individuals who only appear in one of the periods can introduce bias if their entry or exit from the dataset is related to the treatment or the outcome variable.```


Now for convenience, say that whether an individual has both the data of pre and post treatment, or has partial(i.e. only pre or only post) is determined totally randomly. Would it still be problematic to include individuals with only partial data?

**Tags:** difference-in-difference

> **Comments:**
> **username_1**: It will not necessarily do so but it can. The short answer is that it depends. In order to get a better answer you have to ask a more well designed question. A very important issue is why the missingness occurs. If the missingness is systematic then your estimate of what the treated units would be like had they not been treated could very well be biased and this would result in bias of treatment effect.  
> **username_2**: @JesperHybel consider maybe expanding the comment into an answer  

---

## 0 answers

