---
title: "Regression Stuff"
subtitle: "EC 607, Set 05"
author: "Edward Rubin"
date: "Spring 2020"
output:
  xaringan::moon_reader:
    css: ['default', 'metropolis', 'metropolis-fonts', 'my-css.css']
    # self_contained: true
    nature:
      highlightStyle: github
      highlightLines: true
      countIncrementalSlides: false
---
class: inverse, middle

```{r, setup, include = F}
# devtools::install_github("dill/emoGG")
library(pacman)
p_load(
  broom, tidyverse,
  ggplot2, ggthemes, ggforce, ggridges,
  latex2exp, viridis, extrafont, gridExtra,
  kableExtra, snakecase, janitor,
  data.table, dplyr,
  lubridate, knitr,
  estimatr, here, magrittr
)
# Define pink color
red_pink <- "#e64173"
turquoise <- "#20B2AA"
orange <- "#FFA500"
red <- "#fb6107"
blue <- "#3b3b9a"
green <- "#8bb174"
grey_light <- "grey70"
grey_mid <- "grey50"
grey_dark <- "grey20"
purple <- "#6A5ACD"
slate <- "#314f4f"
# Dark slate grey: #314f4f
# Knitr options
opts_chunk$set(
  comment = "#>",
  fig.align = "center",
  fig.height = 7,
  fig.width = 10.5,
  warning = F,
  message = F
)
opts_chunk$set(dev = "svg")
options(device = function(file, width, height) {
  svg(tempfile(), width = width, height = height)
})
options(crayon.enabled = F)
options(knitr.table.format = "html")
# A blank theme for ggplot
theme_empty <- theme_bw() + theme(
  line = element_blank(),
  rect = element_blank(),
  strip.text = element_blank(),
  axis.text = element_blank(),
  plot.title = element_blank(),
  axis.title = element_blank(),
  plot.margin = structure(c(0, 0, -0.5, -1), unit = "lines", valid.unit = 3L, class = "unit"),
  legend.position = "none"
)
theme_simple <- theme_bw() + theme(
  line = element_blank(),
  panel.grid = element_blank(),
  rect = element_blank(),
  strip.text = element_blank(),
  axis.text.x = element_text(size = 18, family = "STIXGeneral"),
  axis.text.y = element_blank(),
  axis.ticks = element_blank(),
  plot.title = element_blank(),
  axis.title = element_blank(),
  # plot.margin = structure(c(0, 0, -1, -1), unit = "lines", valid.unit = 3L, class = "unit"),
  legend.position = "none"
)
theme_axes_math <- theme_void() + theme(
  text = element_text(family = "MathJax_Math"),
  axis.title = element_text(size = 22),
  axis.title.x = element_text(hjust = .95, margin = margin(0.15, 0, 0, 0, unit = "lines")),
  axis.title.y = element_text(vjust = .95, margin = margin(0, 0.15, 0, 0, unit = "lines")),
  axis.line = element_line(
    color = "grey70",
    size = 0.25,
    arrow = arrow(angle = 30, length = unit(0.15, "inches")
  )),
  plot.margin = structure(c(1, 0, 1, 0), unit = "lines", valid.unit = 3L, class = "unit"),
  legend.position = "none"
)
theme_axes_serif <- theme_void() + theme(
  text = element_text(family = "MathJax_Main"),
  axis.title = element_text(size = 22),
  axis.title.x = element_text(hjust = .95, margin = margin(0.15, 0, 0, 0, unit = "lines")),
  axis.title.y = element_text(vjust = .95, margin = margin(0, 0.15, 0, 0, unit = "lines")),
  axis.line = element_line(
    color = "grey70",
    size = 0.25,
    arrow = arrow(angle = 30, length = unit(0.15, "inches")
  )),
  plot.margin = structure(c(1, 0, 1, 0), unit = "lines", valid.unit = 3L, class = "unit"),
  legend.position = "none"
)
theme_axes <- theme_void() + theme(
  text = element_text(family = "Fira Sans Book"),
  axis.title = element_text(size = 18),
  axis.title.x = element_text(hjust = .95, margin = margin(0.15, 0, 0, 0, unit = "lines")),
  axis.title.y = element_text(vjust = .95, margin = margin(0, 0.15, 0, 0, unit = "lines")),
  axis.line = element_line(
    color = grey_light,
    size = 0.25,
    arrow = arrow(angle = 30, length = unit(0.15, "inches")
  )),
  plot.margin = structure(c(1, 0, 1, 0), unit = "lines", valid.unit = 3L, class = "unit"),
  legend.position = "none"
)
theme_set(theme_gray(base_size = 20))
# Column names for regression results
reg_columns <- c("Term", "Est.", "S.E.", "t stat.", "p-Value")
# Function for formatting p values
format_pvi <- function(pv) {
  return(ifelse(
    pv < 0.0001,
    "<0.0001",
    round(pv, 4) %>% format(scientific = F)
  ))
}
format_pv <- function(pvs) lapply(X = pvs, FUN = format_pvi) %>% unlist()
# Tidy regression results table
tidy_table <- function(x, terms, highlight_row = 1, highlight_color = "black", highlight_bold = T, digits = c(NA, 3, 3, 2, 5), title = NULL) {
  x %>%
    tidy() %>%
    select(1:5) %>%
    mutate(
      term = terms,
      p.value = p.value %>% format_pv()
    ) %>%
    kable(
      col.names = reg_columns,
      escape = F,
      digits = digits,
      caption = title
    ) %>%
    kable_styling(font_size = 20) %>%
    row_spec(1:nrow(tidy(x)), background = "white") %>%
    row_spec(highlight_row, bold = highlight_bold, color = highlight_color)
}
```

```{css, echo = F, eval = T}
@media print {
  .has-continuation {
    display: block !important;
  }
}
```

# Prologue

---
name: previously

# Schedule

## Last time: Inference and simulation

Let's review using a quote from *MHE*

> We've chosen to start with the .hi[asymptotic approach to inference] because modern empirical work typically leans heavily on the large-sample theory that lies behind robust variance formulas. The .hi[payoff is valid inference under weak assumptions], in particular, a framework that makes sense for our less-than-literal approach to regression models. On the other hand, the .hi[large-sample approach is not without its dangers]...

.grey-light[.small[*MHE*, p. 48 (emphasis added)]]

---
name: schedule

# Schedule
## Today

Regression and causality
<br>.note[Read] *MHE* 3.2

## Upcoming

Assignment \#1
---
name: advice
layout: false
class: clear, middle

.attn[Advice] Make sure you're taking a few minutes for personal health..super[.pink[†]]

.footnote[.pink[†] *health* = physical, mental, and spiritual. Also: Do a better job than I do.]
---
layout: false
class: inverse, middle

# Regression talk
## Saturated models

---
layout: true

# Regression talk
## Saturated models

A .attn[saturated model] is a regression model that includes a discrete (indicator) variable for each set of values the explanatory variables can take.

---
name: saturated

--

For discrete regressors, saturated models are pretty straightforward.

--

.ex[Example] For the relationship between .purple[Wages] and .orange[College Graduation],

--

$$
\begin{align}
  \color{#6A5ACD}{\text{Wages}_{i}} = \alpha + \beta \, \color{#FFA500}{\mathbb{I}\left\{ \text{College Graduate} \right\}_i} + \varepsilon_i
\end{align}
$$

---

For multi-valued variables, you need an indicator for each potential value.

--

.ex[Example.sub[2]] Regressing .purple[Wages] on .turquoise[Schooling] $\left(\color{#20B2AA}{s_i \in \left\{0,1,2,\ldots T \right\}}\right)$.

--

$$
\begin{align}
  \color{#6A5ACD}{\text{Wages}_{i}} = \alpha +
  \beta_1 \, \color{#20B2AA}{\mathbb{I}\left\{ s_i = 1 \right\}_i}  +
  \beta_2 \, \color{#20B2AA}{\mathbb{I}\left\{ s_i = 2 \right\}_i}  +
  \cdots +
  \beta_T \, \color{#20B2AA}{\mathbb{I}\left\{ s_i = T \right\}_i}  +
  \varepsilon_i
\end{align}
$$

--

Here, $\color{#20B2AA}{s_i=0}$ is our reference level; $\beta_j$ is the effect of $\color{#20B2AA}{j}$ years of schooling.

--

$$
\begin{align}
  \mathop{E}\left[ \color{#6A5ACD}{\text{Wages}_i} \mid \color{#20B2AA}{s_i = j} \right] - \mathop{E}\left[ \color{#6A5ACD}{\text{Wages}_i} \mid \color{#20B2AA}{s_i = 0} \right] = \alpha + \beta_j - \alpha = \beta_j
\end{align}
$$
---
layout: true

# Regression talk
## Saturated models

---

.qa[Q] Why focus on saturated models?

--

.qa[A] .hi-slate[Saturated models perfectly fit the CEF]
--
 because the CEF is a linear function of the dummy variables—a special case of the linear CEF theorem.

---
If you have multiple explanatory variables, you need .attn[interactions].

--

.ex[Example.sub[3]] Regressing .purple[Wages] on .orange[College Graduation] and .red[Gender].

--

$$
\begin{align}
  \color{#6A5ACD}{\text{Wages}_{i}} = \alpha &+ \beta_1 \, \color{#FFA500}{\mathbb{I}\left\{ \text{College Graduate} \right\}_i} + \beta_2 \, \color{#fb6107}{\mathbb{I}\left\{ \text{Female} \right\}_i}
  \\
  &+ \beta_3 \, \color{#FFA500}{\mathbb{I}\left\{ \text{College Graduate} \right\}_i} \times \color{#fb6107}{\mathbb{I}\left\{ \text{Female} \right\}_i} +  \varepsilon_i
\end{align}
$$

--

Here, the uninteracted terms $\left( \beta_1\:\&\: \beta_2 \right)$ are called .attn[main effects]; $\beta_3$ gives the effect of the .attn[interaction].

--

$$
\begin{align}
  \mathop{E}\left[ \color{#6A5ACD}{\text{Wages}_i} | \color{#FFA500}{\text{College Graduate}_i = 0},\, \color{#fb6107}{\text{Female}_i = 0} \right] &= \alpha \\
  \mathop{E}\left[ \color{#6A5ACD}{\text{Wages}_i} | \color{#FFA500}{\text{College Graduate}_i = 1},\, \color{#fb6107}{\text{Female}_i = 0} \right] &= \alpha + \beta_1 \\
  \mathop{E}\left[ \color{#6A5ACD}{\text{Wages}_i} | \color{#FFA500}{\text{College Graduate}_i = 0},\, \color{#fb6107}{\text{Female}_i = 1} \right] &= \alpha + \beta_2 \\
  \mathop{E}\left[ \color{#6A5ACD}{\text{Wages}_i} | \color{#FFA500}{\text{College Graduate}_i = 1},\, \color{#fb6107}{\text{Female}_i = 1} \right] &= \alpha + \beta_1 + \beta_2 + \beta_3
\end{align}
$$

---

The CEF can take on four possible values,

$$
\begin{align}
  \mathop{E}\left[ \color{#6A5ACD}{\text{Wages}_i} | \color{#FFA500}{\text{College Graduate}_i = 0},\, \color{#fb6107}{\text{Female}_i = 0} \right] &= \alpha \\
  \mathop{E}\left[ \color{#6A5ACD}{\text{Wages}_i} | \color{#FFA500}{\text{College Graduate}_i = 1},\, \color{#fb6107}{\text{Female}_i = 0} \right] &= \alpha + \beta_1 \\
  \mathop{E}\left[ \color{#6A5ACD}{\text{Wages}_i} | \color{#FFA500}{\text{College Graduate}_i = 0},\, \color{#fb6107}{\text{Female}_i = 1} \right] &= \alpha + \beta_2 \\
  \mathop{E}\left[ \color{#6A5ACD}{\text{Wages}_i} | \color{#FFA500}{\text{College Graduate}_i = 1},\, \color{#fb6107}{\text{Female}_i = 1} \right] &= \alpha + \beta_1 + \beta_2 + \beta_3
\end{align}
$$

and the specification of our saturated regression model

$$
\begin{align}
  \color{#6A5ACD}{\text{Wages}_{i}} = \alpha &+ \beta_1 \, \color{#FFA500}{\mathbb{I}\left\{ \text{College Graduate} \right\}_i} + \beta_2 \, \color{#fb6107}{\mathbb{I}\left\{ \text{Female} \right\}_i}
  \\
  &+ \beta_3 \, \color{#FFA500}{\mathbb{I}\left\{ \text{College Graduate} \right\}_i} \times \color{#fb6107}{\mathbb{I}\left\{ \text{Female} \right\}_i} +  \varepsilon_i
\end{align}
$$

does not restrict the CEF at all.

---
layout: true
# Regression talk
## Model specification

---
name: specification

*Saturated models* sit at one extreme of the model-specification spectrum, with *linear, uninteracted models* occupying the opposite extreme.

.pull-left[
.attn[Saturated models]
- Fit CEF $(+)$
- Complex $(-)$
  - Many dummies
  - Many interactions
]

.pull-right[
.attn[Plain, linear models]
- Linear approximations $(-)$
- Simple $(+)$
]

--

Don't forget the there are many options in between—though some make less sense than others (_e.g._, interactions without main effects).

---

.note[Note] Saturated models perfectly fit the CEF regardless of $\text{Y}_{i}$'s distribution.

--

Continuous, linear probability, logged, non-negative—it works for all.
---
layout: false
class: clear, middle

Now back to causality...
---
class: inverse, middle

# Regression and causality

---
layout: true
# Regression and causality
## The return of causality

---
name: causal_reg

We've spent the last few lectures developing properties/understanding of (.hi-slate[1]) the CEF and (.hi-slate[2]) least-squares regression.

Let's return to our main goal of the course...

.qa[Q] When can we actually interpret a regression as .hi[causal]?.super[.pink[†]]

.footnote[.pink[†] *Hint:* There is no ".mono[reg y x, causal]" command in Stata.]

--

.qa[A] A regression is causal when the CEF it approximates is causal.

---

Great... thanks.

.qa[Q] So when is a CEF causal?

--

.qa[A] First, return to the potential-outcomes framework, describing hypothetical outcomes.

> A CEF is causal when it describes .hi[differences in average potential outcomes] for a fixed reference population.

.grey-light[*MHE*, p. 52 (emphasis added)]

--

Let's work through this "definition" of causal CEFs with an example.

---
layout: true
# Regression and causality
## Causal CEFs

---
name: causal_cef

.ex[Example] The (causal) effect of schooling on income.

--

The causal effect of schooling for individual $i$ would tell us how $i$'s
<br>.hi-purple[earnings] $\color{#6A5ACD}{\text{Y}_{i}}$ would change if we varied $i$'s .hi-pink[level of schooling] $\color{#e64173}{s_i}$.

--

Previously, we discussed how experiments randomly assign treatment to .it[ensure the variable of interest is independent of potential outcomes].

--

Now we would like to .hi-slate[extend this framework] to

1. variables that take on .hi-slate[more than two values]
2. situations that requrire us to .hi-slate[hold many covariates constant] in order to achieve a valid causal interpretation

---

The idea of *holding (many) covariates constant* brings us to one of the cornerstones of applied econometrics: the .attn[conditional independence assumption (CIA)] (also called *selection on observables*).

---
layout: true
# Regression and causality
## The conditional independence assumption

---
name: cia

.note[Definition(s)]

- Conditional on some set of covariates $\text{X}_{i}$, selection bias disappears.

--

- Conditional on $\text{X}_{i}$, potential outcomes $\left( \color{#6A5ACD}{\text{Y}_{0i}},\, \color{#6A5ACD}{\text{Y}_{1i}} \right)$ are independent of treatment status $\left( \color{#e64173}{\text{D}_{i}} \right)$.

$$
\begin{align}
  \def\ci{\perp\mkern-10mu\perp}
  \left\{ \color{#6A5ACD}{\text{Y}_{0i}},\,\color{#6A5ACD}{\text{Y}_{1i}} \right\} \ci  \color{#e64173}{\text{D}_{i}} | \text{X}_{i}
\end{align}
$$

--

To see how CIA eliminates selection bias...

Selection bias $= \mathop{E}\left[ \color{#6A5ACD}{\text{Y}_{0i}} \mid \text{X}_{i},\, \color{#e64173}{\text{D}_{i}= 1} \right] - \mathop{E}\left[ \color{#6A5ACD}{\text{Y}_{0i}} \mid \text{X}_{i},\, \color{#e64173}{\text{D}_{i}= 0} \right]$
--
<br>.white[Selection bias] $= \mathop{E}\left[ \color{#6A5ACD}{\text{Y}_{0i}} \mid \text{X}_{i} \right] - \mathop{E}\left[ \color{#6A5ACD}{\text{Y}_{0i}} \mid \text{X}_{i} \right]$
--
<br>.white[Selection bias] $= 0$

---
name: cia_binary

Another way you'll hear CIA: After controlling for some set of variables $\text{X}_{i}$, treatment assignment is .it[.hi-slate[as good as random]].

--

To see how this assumption.super[.pink[†]] buys us a causal interpretation, write out our old difference in means—but now condition on $\text{X}_{i}$.

.footnote[.pink[†] Another way to think about econometric assumptions is as requirements.]

--

$$
\begin{align}
  &\mathop{E}\left[ \color{#6A5ACD}{\text{Y}_{i}} \mid \text{X}_{i},\, \color{#e64173}{\text{D}_{i}=1} \right] - \mathop{E}\left[ \color{#6A5ACD}{\text{Y}_{i}} \mid \text{X}_{i},\, \color{#e64173}{\text{D}_{i}=0} \right] \\[0.5em]
  &= \mathop{E}\left[ \color{#6A5ACD}{\text{Y}_{1i}} \mid \text{X}_{i} \right] - \mathop{E}\left[ \color{#6A5ACD}{\text{Y}_{0i}} \mid \text{X}_{i} \right] \\[0.5em]
  &= \mathop{E}\left[ \color{#6A5ACD}{\text{Y}_{1i}} - \color{#6A5ACD}{\text{Y}_{0i}} \mid \text{X}_{i} \right]
\end{align}
$$

--

Even randomized experiments need the CIA—_e.g._, the STAR experiment's .it[within-school] randomization.

---
name: cia_multi

Now let's extend this framework to .hi-slate[multi-valued explanatory variables].

--

.ex[Example continued] .pink[Schooling] $\left( \color{#e64173}{s_i} \right)$ takes on integers $\in\left\{ 0,\,1,\,\ldots,\, T \right\}$.

We want to know the effect of an individual's .pink[schooling] on her .purple[wages] $\left( \color{#6A5ACD}{\text{Y}_{i}} \right)$.

--

Previously, $\color{#6A5ACD}{\text{Y}_{1i}}$ denoted individual $i$'s outcome under treatment.

Now, $\color{#6A5ACD}{\text{Y}_{si}}$ denotes individual $i$'s outcome .pink[with] $\color{#e64173}{s}$ .pink[years of schooling].

--

Let each individual have her own function between .pink[schooling] and .purple[earnings].

$$
\begin{align}
  \color{#6A5ACD}{\text{Y}_{si}} \equiv \mathop{f_i}(\color{#e64173}{s})
\end{align}
$$

$\mathop{f_i}(\color{#e64173}{s})$ answers exactly the type of causal questions that we want to answer.

---

Extending the CIA to this multi-valued setting...

.center[
$\color{#6A5ACD}{\text{Y}_{si}} \ci \color{#e64173}{s_i} \mid \text{X}_{i}\enspace$ for all $\color{#e64173}{s}$
]

--

If we apply the CIA to $\color{#6A5ACD}{\text{Y}_{si}} \equiv \mathop{f_i}(\color{#e64173}{s})$, we define the .it[average causal effect] of a one-year increase in .pink[schooling] as
$$
\begin{align}
  \mathop{E}\left[ \mathop{f_i}(\color{#e64173}{s}) - \mathop{f_i}(\color{#e64173}{s-1}) \mid \text{X}_{i} \right]
\end{align}
$$

--

However, the data only contain one realization of $f_i(\color{#e64173}{s})$ per $i$—we only see $f_i(\color{#e64173}{s})$ evaluated at exactly one value of $\color{#e64173}{s}$ per $i$, _i.e._, $\color{#6A5ACD}{\text{Y}_{i}} = f_i(\color{#e64173}{s_i})$.

--

The CIA to the rescue!
--
 Conditional on $\text{X}_{i}$, $\color{#6A5ACD}{\text{Y}_{si}}$ and $\color{#e64173}{s_i}$ are independent.

---

The CIA to the rescue! Conditional on $\text{X}_{i}$, $\color{#6A5ACD}{\text{Y}_{si}}$ and $\color{#e64173}{s_i}$ are independent.

$$
\begin{align}
  &\mathop{E}\left[ \color{#6A5ACD}{\text{Y}_{i}} \mid \text{X}_{i},\, \color{#e64173}{s_i = s} \right] - \mathop{E}\left[ \color{#6A5ACD}{\text{Y}_{i}} \mid \text{X}_{i},\, \color{#e64173}{s_i = s-1} \right] \\[0.5em]
  &=\mathop{E}\left[ \color{#6A5ACD}{\text{Y}_{si}} \mid \text{X}_{i},\, \color{#e64173}{s_i = s} \right] - \mathop{E}\left[ \color{#6A5ACD}{\text{Y}_{(s-1)i}} \mid \text{X}_{i},\, \color{#e64173}{s_i = s-1} \right] \\[0.5em]
  &=\mathop{E}\left[ \color{#6A5ACD}{\text{Y}_{si}} \mid \text{X}_{i} \right] - \mathop{E}\left[ \color{#6A5ACD}{\text{Y}_{(s-1)i}} \mid \text{X}_{i} \right] \\[0.5em]
  &=\mathop{E}\left[ \color{#6A5ACD}{\text{Y}_{si}} - \color{#6A5ACD}{\text{Y}_{(s-1)i}} \mid \text{X}_{i} \right] \\[0.5em]
  &=\mathop{E}\left[ \mathop{f_i}(\color{#e64173}{s}) - \mathop{f_i}(\color{#e64173}{s-1}) \mid \text{X}_{i} \right]
\end{align}
$$

--

With the CIA, a difference in conditional averages allows causal interpretations.

---

.ex[Example] The causal effect of high-school graduation is

--

.big-left[
$\mathop{E}\left[ \color{#6A5ACD}{\text{Y}_{i}} \mid \text{X}_{i},\, \color{#e64173}{s_i = 12} \right] -   \mathop{E}\left[ \color{#6A5ACD}{\text{Y}_{i}} \mid \text{X}_{i},\, \color{#e64173}{s_i = 11} \right]$
]
--
.big-left[
$=\mathop{E}\left[ f_i(\color{#e64173}{12}) \mid \text{X}_{i},\, \color{#e64173}{s_i = 12} \right] -   \mathop{E}\left[ f_i(\color{#e64173}{11}) \mid \text{X}_{i},\, \color{#e64173}{s_i = 11} \right]$
]
--
.big-left[
$=\mathop{E}\left[ f_i(\color{#e64173}{12}) \mid \text{X}_{i},\, \color{#e64173}{s_i = 12} \right] -   \mathop{E}\left[ f_i(\color{#e64173}{11}) \mid \text{X}_{i},\, \color{#e64173}{s_i = 12} \right]$  .grey-light[(from CIA)]
]
--
.big-left[
$=\mathop{E}\left[ f_i(\color{#e64173}{12}) - f_i(\color{#e64173}{11}) \mid \text{X}_{i},\, \color{#e64173}{s_i = 12} \right]$
]
--
.big-left[
$=$ The average causal effect of graduation .it[for graduates]
]
--
.big-left[
$=\mathop{E}\left[ f_i(\color{#e64173}{12}) - f_i(\color{#e64173}{11}) \mid \text{X}_{i} \right]$ .grey-light[(CIA again)]
]
--
.big-left[
$=$ The (conditional) average causal effect of graduation .it[at] $X_{i}$
]

---

.qa[Q] What about the .hi-slate[unconditional] average causal effect of graduation?

--

.qa[A] First, remember what we just showed...

--

$$
\begin{align}
  \mathop{E}\left[ \color{#6A5ACD}{\text{Y}_{i}} \mid \text{X}_{i},\, \color{#e64173}{s_i = 12} \right] -  \mathop{E}\left[ \color{#6A5ACD}{\text{Y}_{i}} \mid \text{X}_{i},\, \color{#e64173}{s_i = 11} \right] = \mathop{E}\left[ f_i(\color{#e64173}{12}) - f_i(\color{#e64173}{11}) \mid \text{X}_{i} \right]
\end{align}
$$

--

Now take the expected value of both sides and apply the LIE.

.big-left[
$E\!\bigg(\mathop{E}\left[ \color{#6A5ACD}{\text{Y}_{i}} \mid \text{X}_{i},\, \color{#e64173}{s_i = 12} \right] -  \mathop{E}\left[ \color{#6A5ACD}{\text{Y}_{i}} \mid \text{X}_{i},\, \color{#e64173}{s_i = 11} \right] \bigg)$
]
.big-left[
$=E\! \bigg( \mathop{E}\left[ f_i(\color{#e64173}{12}) - f_i(\color{#e64173}{11}) \mid \text{X}_{i}\right] \bigg)$
]
--
.big-left[
$=\mathop{E}\left[ f_i(\color{#e64173}{12}) - f_i(\color{#e64173}{11}) \right]$ .grey-light[(Iterating expectations)]
]

---

.note[Takeaways]

1. Conditional independence gives our parameters .hi[causal interpretations] (elminating selection bias).

--

2. The interpretation changes slightly—without iterating expectations, we have .hi[conditional average treatment effects].

--

3. The CIA is challenging—you need to know which set of covariates $\left( \text{X}_{i} \right)$ leads to .hi[as-good-as-random residual variation in your treatment].

--

4. The idea of conditioning on observables to match *comparable* individuals introduces us to .hi[matching estimators]—comparing groups of individuals with the same covariate values.

---
layout: true

# Regression and causality
## From the CIA to regression

---
name: cia_reg

Conditional independence fits into our regression framework in two ways.

--

1. If we assume $f_i(\color{#e64173}{s})$ is (.hi-slate[A]) linear in $\color{#e64173}{s}$ and (.hi-slate[B]) equal across all individuals except for an additive error, linear regression estimates $f(\color{#e64173}{s})$.

--

2. If we allow $f_i(\color{#e64173}{s})$ to be nonlinear in $\color{#e64173}{s}$ and heterogeneous across $i$, regression provides a weighted average of individual-specific differences $f_i(\color{#e64173}{s}) - f_i(\color{#e64173}{s-1})$..super[.pink[†]]

.footnote[.pink[†] Leads to a matching-style estimator.]

--

Let's start with the 'easier' case: a linear, constant-effects (causal) model.

---

Let $f_i(\color{#e64173}{s})$ be linear in $\color{#e64173}{s}$ and equal across $i$ except for an error term, _e.g._,

$$
\begin{align}
  f_i(\color{#e64173}{s}) = \alpha + \rho \color{#e64173}{s} + \eta_i \tag{A}
\end{align}
$$

--

Substitute in our observed value of $\color{#e64173}{s_i}$ and the outcome $\color{#6A5ACD}{\text{Y}_{i}}$

$$
\begin{align}
  \color{#6A5ACD}{\text{Y}_{i}} = \alpha + \rho \color{#e64173}{s_i} + \eta_i \tag{B}
\end{align}
$$

--

While $\rho$ in $(\text{A})$ is explicitly causal, regression-based estimates of $\rho$ in $(\text{B})$ need not be causal (selection/OVB for endogenous $s_i$).

---

Continuing with our linear, constant-effect causal model...

$$
\begin{align}
  f_i(\color{#e64173}{s}) = \alpha + \rho \color{#e64173}{s} + \eta_i \tag{A}
\end{align}
$$

Now impose the conditional independence assumption for covariates $\text{X}_{i}$.

$$
\begin{align}
  \eta_i = \text{X}_{i}'\gamma + \nu_i \tag{C}
\end{align}
$$

where $\gamma$ is a vector of population coefficients from regressing $\eta_i$ on $\text{X}_{i}$.

--

.note[Note] Least-squares regression implies

1. $\mathop{E}\left[ \eta_i \mid \text{X}_{i} \right] = \text{X}_{i}'\gamma$
1. $\text{X}_{i}$ is uncorrelated with $\nu_i$.

---

Now write out the conditional expectation function of $f_i(\color{#e64173}{s})$ on $\text{X}_{i}$ and $\color{#e64173}{s_i}$.

.big-left[
$\mathop{E}\left[ f_i(\color{#e64173}{s}) \mid \text{X}_{i},\, \color{#e64173}{s_i} \right]$
]
.big-left[
$=\mathop{E}\left[ f_i(\color{#e64173}{s}) \mid \text{X}_{i} \right]$ .grey-light[(CIA)]
]
--
.big-left[
$=\mathop{E}\left[ \alpha + \rho \color{#e64173}{s_i} + \eta_i \mid \text{X}_{i} \right]$
]
--
.big-left[
$=\alpha + \rho \color{#e64173}{s_i} + \mathop{E}\left[ \eta_i \mid \text{X}_{i} \right]$
]
--
.big-left[
$=\alpha + \rho \color{#e64173}{s_i} + \text{X}_{i}'\gamma$ .grey-light[(Least-squares regression)]
]

--

The CEF of $f_i(\color{#e64173}{s_i})$ is linear, which means that the (right.super[.pink[†]]) population regression will be the CEF.

.footnote[
.pink[†] Here, "right" means conditional on $\text{X}_{i}$.
]

---

Thus, the linear causal (regression) model is
$$
\begin{align}
  \text{Y}_{i} = \alpha + \rho \color{#e64173}{s_i} + \text{X}_{i}'\gamma + \nu_i
\end{align}
$$

The residual $\nu_i$ is uncorrelated with

1. $\color{#e64173}{s_i}$ (from the CIA)
2. $\text{X}_{i}$ (from defining $\gamma$ via the regression of $\eta$ on $\text{X}_{i}$)

The coefficient $\rho$ gives the causal effect of $\color{#e64173}{s_i}$ on $\color{#6A5ACD}{\text{Y}_{i}}$.

---

As Angrist and Pischke note, this .hi[conditional-independence assumption] (.it[a.k.a.] the selection-on-observables assumption) is the cornerstone of modern empirical work in economics—and many other disciplines.

Nearly any empirical application that wants a causal interpretation involves a (sometimes implicit) argument that .hi[conditional on some set of covariates, treatment is as-good-as random].

--

.ex[Part of our job:] Reasoning through the validity of this assumption.

---
layout: true
# Regression and causality
## CIA example

---
name: example

Let's continue with the returns to graduation $\left( \text{G}_i \right)$.

Let's imagine

1. Women are more likely to graduate.
1. Everyone receives the same return to graduation.
1. Women receive lower wages across the board.

---

First, we need to generate some data.

```{r, ex_data, eval = T}
# Set seed
set.seed(12345)
# Set sample size
n <- 1e4
# Generate data
ex_df <- tibble(
  female = rep(c(0, 1), each = n/2),
  grad = runif(n, min = female/3, max = 1) %>% round(0),
  wage = 100 - 25 * female + 5 * grad + rnorm(n, sd = 3)
)
```

---

Now we can estimate our naïve regression

$$
\begin{align}
  \text{Wage}_i = \alpha + \beta \text{Grad}_i + \varepsilon_i
\end{align}
$$

--

`lm(wage ~ grad, data = ex_df)`


```{r, echo = F}
lm(wage ~ grad, data = ex_df) %>% tidy() %>%
  select(1:4) %>%
  mutate(term = c("Intercept", "Graduate")) %>%
  kable(col.names = c("", "Coef.", "S.E.", "t stat"), digits = 2)
```

--

Maybe we should have plotted our data...

---
layout: false
class: clear, center

```{r, ex_plot1, echo = F}
# Plot
ggplot(
  data = ex_df %>% mutate(Gender = factor(female, labels = c("Female", "Male"))),
  aes(x = grad, y = wage)
) +
geom_jitter(alpha = 0.3, width = 0.1, shape = 1, size = 2.5) +
geom_smooth(method = lm, se = F, color = orange, size = 0.5) +
annotate(
  geom = "point",
  x = c(0, 1),
  y = c(mean(filter(ex_df, grad == 0)$wage), mean(filter(ex_df, grad == 1)$wage)),
  color = orange,
  size = 6
) +
scale_x_continuous("Graduate", breaks = c(0, 1)) +
ylab("Wage") +
theme_pander(base_family = "Fira Sans Book", base_size = 20)
```

---
class: clear, middle

We're still missing something...

---
class: clear, center


```{r, ex_plot2, echo = F, eval = T}
# Plot
ggplot(
  data = ex_df %>% mutate(Gender = factor(female, labels = c("Female", "Male"))),
  aes(x = grad, y = wage)
) +
geom_jitter(
  aes(color = Gender),
  alpha = 0.2, width = 0.1,
  shape = 1, size = 2.5
) +
geom_smooth(
  data = ex_df %>% filter(female == 1),
  method = lm, se = F, size = 0.5,
  color = red_pink
) +
geom_smooth(
  data = ex_df %>% filter(female == 0),
  method = lm, se = F, size = 0.5,
  color = slate
) +
annotate(
  geom = "point",
  x = c(0, 1),
  y = c(mean(filter(ex_df, grad == 0 & female == 1)$wage), mean(filter(ex_df, grad == 1 & female == 1)$wage)),
  color = red_pink,
  size = 6
) +
annotate(
  geom = "point",
  x = c(0, 1),
  y = c(mean(filter(ex_df, grad == 0 & female == 0)$wage), mean(filter(ex_df, grad == 1 & female == 0)$wage)),
  color = slate,
  size = 6
) +
scale_x_continuous("Graduate", breaks = c(0, 1)) +
ylab("Wage") +
theme_pander(base_family = "Fira Sans Book", base_size = 20) +
scale_color_manual(values = c(slate, red_pink)) +
theme(legend.position = "none")
```

---
# Regression and causality
## CIA example

Now we can estimate our causal regression

$$
\begin{align}
  \text{Wage}_i = \alpha + \beta_1 \text{Grad}_i + \beta_2 \text{Female}_i + \varepsilon_i
\end{align}
$$

--

`lm(wage ~ grad + female, data = ex_df)`


```{r, echo = F}
lm(wage ~ grad + female, data = ex_df) %>% tidy() %>%
  select(1:4) %>%
  mutate(term = c("Intercept", "Graduate", "Female")) %>%
  kable(col.names = c("", "Coef.", "S.E.", "t stat"), digits = 2)
```

---
layout: false
# Table of contents

.pull-left[
### Admin
.smaller[

1. [Last time](#previously)
1. [Schedule](#schedule)
1. [Advice](#advice)
]

]

.pull-right[
### Regression
.smaller[

1. [Saturated models](#saturated)
1. [Model specification](#specification)
1. [Causal regressions](#causal_reg)
1. [Causal CEFs](#causal_cef)
1. [Conditional independence assumption](#cia)
  - [Binary treatment](#cia_binary)
  - [Multi-valued treatment](#cia_multi)
  - [Regression](#cia_reg)
  - [Example](#example)

]
]
---
exclude: true

```{r, generate pdfs, include = F, eval = F}
pagedown::chrome_print("05-regression-stuff.html", output = "05-regression-stuff.pdf")
pagedown::chrome_print("05-regression-stuff.html", output = "05-regression-stuff-nopause.pdf")
```