Class 9: Diff-in-diff II

# In-person session 9

**October 17, 2022**

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# Plan for today

.box-3.medium[General questions]

.box-7.medium[Final project]

.box-1.medium[Simple diff-in-diff]

.box-6.medium[Two-way fixed effects]

.box-2.medium[Clusters and sensitivity]

---

layout: false
name: general-questions
class: center middle section-title section-title-3 animated fadeIn

# General questions

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.box-3.large[Should we control for variables to close as many backdoors as possible in our diff-in-diff model?]

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# Design-based identification

.box-inv-3.small[Use randomization to remove confounding]

.center[
<figure>
 <img src="05-slides_files/figure-html/experimental-dag-1.png" alt="RCT DAG" title="RCT DAG" width="60%">
</figure>
]
]

.box-inv-3.small[Use before/after & treatment/control differences to remove confounding]

.center[
<figure>
 <img src="08-slides_files/figure-html/min-wage-dag-1.png" alt="Diff-in-diff DAG" title="Diff-in-diff DAG" width="90%">
</figure>
]
]

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.box-3.large[How does moving time back let us check for parallel trends?]

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.center[
<figure>
 <img src="img/08-class/mm-fig-5-4.png" alt="Mastering Metrics Figure 5.4" title="Mastering Metrics Figure 5.4" width="65%">
</figure>
]

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.box-3.large[Can you conduct diff-in-diff with a binary outcome?]

???

- <https://www.andrewheiss.com/blog/2021/11/08/beta-regression-guide/#1-linear-probability-models>
- <https://bayesf22-notebook.classes.andrewheiss.com/bayes-rules/13-chapter.html#prelude-ii-how-to-interpret-logistic-regression-coefficients>

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.box-3.large[I keep reading about estimates, estimands, and estimators.]

.box-3.large[What are these and are they the same thing?]

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# Estima(and|or|ate)s

.box-inv-3.less-medium[Estimand]

.box-3.sp-after[Theoretical thing you want to know (β)]

.box-inv-3.less-medium[Estimator]

.box-3.sp-after[Process for guessing the thing .small[(e.g., diff-in-diff with interaction term)]]

.box-inv-3.less-medium[Estimate]

---

.center[
<figure>
 <img src="img/09-class/estimandateor.jpg" alt="Estimandateor" title="Estimandateor" width="45%">
</figure>
]

???

<https://twitter.com/WomenInStat/status/1449068312873287682>

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.center[
<figure>
 <img src="img/09-class/flowchart.png" alt="Estimand flowchart" title="Estimand flowchart" width="100%">
</figure>
]

---

.center[
<figure>
 <img src="img/09-class/three-step-process.png" alt="Three-step process" title="Three-step process" width="75%">
</figure>
]

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layout: false
name: final-project
class: center middle section-title section-title-7 animated fadeIn

# Final project

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.box-7.large[Tell us more about the final project!]

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layout: false
name: diff-in-diff
class: center middle section-title section-title-1 animated fadeIn

# Simple diff-in-diff

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---

.box-1.large[Minimum legal drinking age]

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.box-inv-1.medium[MLDA reduction]

.box-1.medium[Two states: Alabama vs. Arkansas]

`$$\begin{aligned}
\text{Mortality}\ =&\ \beta_0 + \beta_1\ \text{Alabama} + \beta_2\ \text{After 1975}\ + \\
&\ \beta_3\ (\text{Alabama} \times \text{After 1975})
\end{aligned}$$`

---

.box-inv-1.medium[Organ donations]

.box-1.medium[Two states: California vs. New Jersey]

`$$\begin{aligned}
\text{Donation rate}\ =&\ \beta_0 + \beta_1\ \text{California} + \beta_2\ \text{After Q22011}\ + \\
&\ \beta_3\ (\text{California} \times \text{After Q22011})
\end{aligned}$$`

---

layout: false
name: twfe
class: center middle section-title section-title-6 animated fadeIn

# Two-way fixed effects (TWFE)

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---

.box-6.medium[Two states: Alabama vs. Arkansas]

`$$\begin{aligned}
\text{Mortality}\ =&\ \beta_0 + \beta_1\ \text{Alabama} + \beta_2\ \text{After 1975}\ + \\
&\ \beta_3\ (\text{Alabama} \times \text{After 1975})
\end{aligned}$$`

---

.box-6.medium[All states: `Treatment == 1` if legal for 18-20-year-olds to drink]

`$$\text{Mortality}\ =\ \beta_0 + \beta_1\ \text{Treatment} + \beta_2\ \text{State} + \beta_3\ \text{Year}$$`

---

`$$\begin{aligned}
\text{Mortality}\ =&\ \beta_0 + \beta_1\ \text{Alabama} + \beta_2\ \text{After 1975}\ + \\
&\ \color{red}{\beta_3}\ (\text{Alabama} \times \text{After 1975})
\end{aligned}$$`

`$$\text{Mortality}\ =\ \beta_0 + \color{red}{\beta_1}\ \text{Treatment} + \beta_2\ \text{State} + \beta_3\ \text{Year}$$`

---

`$$\begin{aligned}
\text{Mortality}\ =&\ \beta_0 + \beta_1\ \text{Alabama} + \beta_2\ \text{After 1975}\ + \\
&\ \color{red}{\beta_3}\ (\text{Alabama} \times \text{After 1975})
\end{aligned}$$`

`$$\text{Mortality}\ =\ \beta_0 + \color{red}{\beta_1}\ \text{Treatment} + \beta_2\ \text{State} + \beta_3\ \text{Year}$$`

`$$\begin{aligned}
\text{Mortality}\ =\ & \beta_0 + \color{red}{\beta_1}\ \text{Treatment} + \beta_2\ \text{State} + \beta_3\ \text{Year}\ +\\
&\beta_4\ (\text{State} \times \text{Year})
\end{aligned}$$`

---

.center[
<figure>
 <img src="img/08-class/mm-tbl-5-2.png" alt="Mastering Metrics Table 5.2" title="Mastering Metrics Table 5.2" width="55%">
</figure>
]

---

`$$\begin{aligned}
\text{Donation rate}\ =&\ \beta_0 + \beta_1\ \text{California} + \beta_2\ \text{After Q22011}\ + \\
&\ \color{red}{\beta_3}\ (\text{California} \times \text{After Q22011})
\end{aligned}$$`

$$
`\begin{aligned}
\text{Donation rate}\ =\ & \beta_0 + \color{red}{\beta_1}\ \text{Treatment}\ + \\
& \beta_2\ \text{State} + \beta_3\ \text{Quarter}
\end{aligned}`
$$

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.box-6.large[What about this staggered treatment stuff?]

???

This is good for ethical reasons!

Blog post

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.box-6.large[What are random effects?]

.box-inv-6[[See this](https://www.andrewheiss.com/blog/2021/12/01/multilevel-models-panel-data-guide/)]

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layout: false
name: sensitivity
class: center middle section-title section-title-2 animated fadeIn

# Clusters and sensitivity

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.box-2.medium.sp-after[What are robust standard errors?]

.box-2.medium.sp-after[What are clustered standard errors?]

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.box-2.medium.sp-after[How do we know when we've got the right confounders in our DAG?]

.box-2.medium[How do we solve the fact that we have so many unknowns in our DAG?]

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.center[
<figure>
 <img src="img/08-class/2020-2021-meme-garnick-1.jpg" alt="OVB" title="OVB" width="40%">
</figure>
]

???

https://owenozier.github.io/teaching/2020-2021-memes