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A repository for the NYUSH graduate seminar based on Brady Neal's Causal Inference Course.
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<h1>Index of /teaching/CausalInf2021</h1>
<pre><img src="/icons/blank.gif" alt="Icon "> <a href="?C=N;O=D">Name</a> <a href="?C=M;O=A">Last modified</a> <a href="?C=S;O=A">Size</a> <a href="?C=D;O=A">Description</a><hr><img src="/icons/back.gif" alt="[PARENTDIR]"> <a href="/teaching/">Parent Directory</a> -
<img src="/icons/layout.gif" alt="[ ]"> <a href="10%20-%20Causal%20Discovery%20from%20Observational%20Data.pdf">10 - Causal Discovery from Observational Data.pdf</a> 2021-02-02 12:36 3.5M
<img src="/icons/layout.gif" alt="[ ]"> <a href="11%20-%20Causal%20Discovery%20from%20Interventions.pdf">11 - Causal Discovery from Interventions.pdf</a> 2021-02-02 12:36 2.9M
<img src="/icons/layout.gif" alt="[ ]"> <a href="12%20-%20Transfer%20Learning%20and%20Transportability.pdf">12 - Transfer Learning and Transportability.pdf</a> 2021-02-02 12:36 3.2M
<img src="/icons/layout.gif" alt="[ ]"> <a href="14%20-%20Counterfactuals%20and%20Mediation.pdf">14 - Counterfactuals and Mediation.pdf</a> 2021-02-02 12:36 3.1M
<img src="/icons/layout.gif" alt="[ ]"> <a href="2%20-%20Potential%20Outcomes.pdf">2 - Potential Outcomes.pdf</a> 2021-02-02 12:36 5.9M
<img src="/icons/layout.gif" alt="[ ]"> <a href="3%20-%20The%20Flow%20of%20Association%20and%20Causation%20in%20Graphs.pdf">3 - The Flow of Association and Causation in Graphs.pdf</a> 2021-02-02 12:36 3.8M
<img src="/icons/layout.gif" alt="[ ]"> <a href="4%20-%20Causal%20Models.pdf">4 - Causal Models.pdf</a> 2021-02-02 12:36 5.8M
<img src="/icons/layout.gif" alt="[ ]"> <a href="5%20-%20Identification.pdf">5 - Identification.pdf</a> 2021-02-02 12:36 4.3M
<img src="/icons/layout.gif" alt="[ ]"> <a href="6%20-%20Estimation.pdf">6 - Estimation.pdf</a> 2021-02-02 12:36 4.0M
<img src="/icons/layout.gif" alt="[ ]"> <a href="7%20-%20Unobserved%20Confounding.pdf">7 - Unobserved Confounding.pdf</a> 2021-02-02 12:36 7.5M
<img src="/icons/layout.gif" alt="[ ]"> <a href="8%20-%20Instrumental%20Variables.pdf">8 - Instrumental Variables.pdf</a> 2021-02-02 12:36 3.4M
<img src="/icons/layout.gif" alt="[ ]"> <a href="9%20-%20Difference-in-Differences.pdf">9 - Difference-in-Differences.pdf</a> 2021-02-02 12:36 3.1M
<img src="/icons/unknown.gif" alt="[ ]"> <a href="CausalCourse%20Syllabus.docx">CausalCourse Syllabus.docx</a> 2021-01-08 14:51 8.4K
<img src="/icons/unknown.gif" alt="[ ]"> <a href="CausalityLecture-30nov2020.pptx">CausalityLecture-30nov2020.pptx</a> 2021-01-22 14:26 36M
<img src="/icons/layout.gif" alt="[ ]"> <a href="Introduction_to_Causal_Inference-Dec17_2020-Neal.pdf">Introduction_to_Causal_Inference-Dec17_2020-Neal.pdf</a> 2021-01-22 14:16 1.0M
<img src="/icons/text.gif" alt="[TXT]"> <a href="presentations.jl.html">presentations.jl.html</a> 2021-02-02 13:04 19K
<img src="/icons/layout.gif" alt="[ ]"> <a href="probability_cheatsheet.pdf">probability_cheatsheet.pdf</a> 2021-01-26 12:44 789K
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<h1 style="font-size:40px">Introduction to Causal Inference</h1>
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<span class="page-subheading" style="font-size:20px">Spring 2021</span>
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<p>This is the NYUSH causal inference course page. This webpage was more-or-less copied from <a
href="https://bradyneal.com">Brady Neal's Site</a>.
Although, the course text is written from a machine learning perspective, this course is meant to be
for anyone with the necessary <a href="#prerequisites">prerequisites</a> who is interested in
learning the basics of causality.
The class attempts to integrate insights from <a
href="https://www.bradyneal.com/which-causal-inference-book">many different fields</a> that
utilize causal inference such as epidemiology, economics, political science, machine learning, etc.
You can see the <a href="#course-schedule-tentative">tentative course schedule</a> below.</p>
<p>You can join the <a
href="https://chat.erlichlab.org/signup_user_complete/?id=d8phzf5nfib99j7f7ahti656gh">course
mattermost</a> where you can easily start discussions with other people who are interested in
causal inference. Email Profs. Erlich or Weslake to meet outside of class hours.
The main <a href="#course-textbook">textbook</a> we'll use for this course is <em>Introduction to
Causal Inference</em> (ICI), which is a book draft that I'll continually update throughout this
course.</p>
<h2 id="course-schedule-tentative">Course Schedule</h2>
<p><strong>Note about slides:</strong> they currently don't work well with Adobe Acrobat, though they
seem to work with other PDF viewers.</p>
<p><strong>Note about videos:</strong> The <a
html="https://www.youtube.com/playlist?list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0">videos for each
week</a> are broken up into several short videos. You must watch ALL of the videos for each week
as part of class preparation. </p>
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<th>Week</th>
<th>Date</th>
<th>Topics</th>
<th>Lecture</th>
<th>Readings</th>
<th>Reading Group Paper</th>
</tr>
</thead>
<tbody>
<tr>
<td>1</td>
<td>Jan 26</td>
<td>Motivation<br />Course Preview<br />Course Information</td>
<td><a href="https://www.youtube.com/watch?v=CfzO4IEMVUk&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=1"
target="_blank" rel="noopener noreferrer">Video</a><br />
<a href="/slides/1%20-%20A%20Brief%20Introduction%20to%20Causal%20Inference.pdf"
download="">Slides</a><br />
<a href="https://www.youtube.com/watch?v=xj-tzrm5Src&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=6"
target="_blank" rel="noopener noreferrer">Info</a>
</td>
<td>Chapter 1 of ICI</td>
<td>None</td>
</tr>
<tr>
<td>2</td>
<td>Feb 2</td>
<td>Potential Outcomes<br />A Complete Example with Estimation</td>
<td><a href="https://www.youtube.com/watch?v=q8x9aetyok0&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=8"
target="_blank" rel="noopener noreferrer">Video</a><br />
<a href="/slides/2%20-%20Potential%20Outcomes.pdf" download="">Slides</a>
</td>
<td>Chapter 2 of ICI</td>
<td><a href="https://www.nature.com/articles/ijo200882" target="_blank"
rel="noopener noreferrer">Does obesity shorten life? The importance of well-defined
interventions to answer causal questions (Hernán & Taubman, 2008)</a></td>
</tr>
<tr>
<td>3</td>
<td>Feb 9</td>
<td>Graphical Models<br /></td>
<td><a href="https://www.youtube.com/watch?v=Go4EkHN_PcA&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=19"
target="_blank" rel="noopener noreferrer">Video</a><br />
<a href="/slides/3%20-%20The%20Flow%20of%20Association%20and%20Causation%20in%20Graphs.pdf"
download="">Slides</a>
</td>
<td>Chapter 3 of ICI</td>
<td><a href="https://ftp.cs.ucla.edu/pub/stat_ser/r483-reprint.pdf" target="_blank"
rel="noopener noreferrer">Does Obesity Shorten Life? Or is it the Soda? On
Non-manipulable Causes (Pearl, 2018)</a></td>
</tr>
<tr>
<td>4</td> <td> Feb 23</td>
<td>Backdoor Adjustment<br />Structural Causal Models</td>
<td><a href="https://www.youtube.com/watch?v=dB8r4Afmobo&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=28"
target="_blank" rel="noopener noreferrer">Video</a><br />
<a href="/slides/4%20-%20Causal%20Models.pdf" download="">Slides</a>
</td>
<td>Chapter 4 of ICI</td>
<td><a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.644.1881&rep=rep1&type=pdf"
target="_blank" rel="noopener noreferrer">Single World Intervention Graphs: A Primer
(Richardson & Robins, 2013)</a></td>
</tr>
<tr>
<td>5</td><td> Mar 2</td>
<td>Randomized Experiments<br />Frontdoor
Adjustment<br /><i>do</i>-calculus<br />Graph-Based Identification</td>
<td><a href="https://www.youtube.com/watch?v=z91LnTDyhtI&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=37"
target="_blank" rel="noopener noreferrer">Video</a><br />
<a href="/slides/5%20-%20Identification.pdf" download="">Slides</a>
</td>
<td>Chapters 5-6 of ICI</td>
<td><a href="https://causalai.net/r60.pdf" target="_blank" rel="noopener noreferrer">On
Pearl's Hierarchy and the Foundations of Causal Inference (Bareinboim et al.,
2020)</a></td>
</tr>
<tr><td>6</td>
<td>Mar 9</td>
<td>Estimation<br /><a href="https://athey.people.stanford.edu" target="_blank"
rel="noopener noreferrer">Susan Athey</a><br />Estimating Heterogeneous
Treatment Effects<br /></td>
<td><a href="https://www.youtube.com/watch?v=YzcOYU-s2t4&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=42"
target="_blank" rel="noopener noreferrer">Video</a><br />
<a href="/slides/6%20-%20Estimation.pdf" download="">Slides</a><br />
<a href="https://www.youtube.com/watch?v=oZoizsX3bts&list=PLoazKTcS0RzZ1SUgeOgc6SWt51gfT80N0&index=7"
target="_blank" rel="noopener noreferrer">Guest Talk</a>
</td>
<td>Chapter 7 of ICI</td>
<td><a href="https://arxiv.org/abs/1906.02120" target="_blank"
rel="noopener noreferrer">Adapting Neural Networks for the Estimation of Treatment
Effects (Shi, Blei, Veitch, 2019)</a></td>
</tr>
<tr><td>7</td>
<td>Mar 16</td>
<td>Unobserved Confounding,<br />Bounds, and<br />Sensitivity Analysis</td>
<td><a href="https://www.youtube.com/watch?v=IXNMYqUsBBQ&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=47"
target="_blank" rel="noopener noreferrer">Video</a><br />
<a href="/slides/7%20-%20Unobserved%20Confounding.pdf" download="">Slides</a>
</td>
<td>Chapter 8 of ICI</td>
<td><a href="https://arxiv.org/abs/2003.01747" target="_blank"
rel="noopener noreferrer">Sense and Sensitivity Analysis: Simple Post-Hoc Analysis
of Bias Due to Unobserved Confounding (Veitch & Zaveri, 2020)</a></td>
</tr>
<tr><td>8</td>
<td>Mar 23</td>
<td>Instrumental Variables</td>
<td><a href="https://www.youtube.com/watch?v=Mco16tUSA-U&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=53"
target="_blank" rel="noopener noreferrer">Video</a><br />
<a href="/slides/8%20-%20Instrumental%20Variables.pdf" download="">Slides</a>
</td>
<td>Chapter 9 of ICI</td>
<td><a href="http://proceedings.mlr.press/v70/hartford17a.html" target="_blank"
rel="noopener noreferrer">Deep IV: A Flexible Approach for Counterfactual Prediction
(Hartford et al., 2017)</a></td>
</tr>
<tr><td>9</td>
<td>Mar 30</td>
<td>Difference-in-Differences<br /><a href="http://economics.mit.edu/faculty/abadie"
target="_blank" rel="noopener noreferrer">Alberto Abadie</a><br />Synthetic Control</td>
<td><a href="https://www.youtube.com/watch?v=tT8xLRS_cRQ&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=58"
target="_blank" rel="noopener noreferrer">Video</a><br />
<a href="/slides/9%20-%20Difference-in-Differences.pdf" download="">Slides</a><br />
<a href="https://www.youtube.com/watch?v=nKzNp-qpE-I&list=PLoazKTcS0RzZ1SUgeOgc6SWt51gfT80N0&index=11"
target="_blank" rel="noopener noreferrer">Guest Talk</a>
</td>
<td>Chapter 10 of ICI</td>
<td><a href="https://www.princeton.edu/~davidlee/wp/RDDEconomics.pdf" target="_blank"
rel="noopener noreferrer">Regression Discontinuity Designs in Economics (Lee &
Lemieux, 2010)</a></td>
</tr>
<tr><td>10</td>
<td>Apr 6</td>
<td>Causal Discovery from Observational Data<br /><a href="http://web.math.ku.dk/~peters/"
target="_blank" rel="noopener noreferrer">Jonas Peters</a></td>
<td><a href="https://www.youtube.com/watch?v=lVE-4deFe7c&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=62"
target="_blank" rel="noopener noreferrer">Video</a><br />
<a href="/slides/10%20-%20Causal%20Discovery%20from%20Observational%20Data.pdf"
download="">Slides</a><br />
</td>
<td>Chapter 11 of ICI</td>
<td><a href="https://www.nature.com/articles/s41467-019-10105-3" target="_blank"
rel="noopener noreferrer">Inferring causation from time series in Earth system
sciences (Runge et al., 2019)</a></td>
</tr>
<tr><td>11</td>
<td>Apr 13</td>
<td>Causal Discovery from Interventions</td>
<td><a href="https://www.youtube.com/watch?v=de2ODel8F1k&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=69"
target="_blank" rel="noopener noreferrer">Video</a><br />
<a href="/slides/11%20-%20Causal%20Discovery%20from%20Interventions.pdf"
download="">Slides</a><br />
</td>
<td>Chapter 12 of ICI<br />(Coming Soon?)</td>
<td><a href="https://arxiv.org/abs/1705.10220" target="_blank"
rel="noopener noreferrer">Permutation-based Causal Inference Algorithms with
Interventions (Wang et al., 2017)</a></td>
</tr>
<tr><td>12</td>
<td>Apr 20 </td>
<td>Transfer Learning<br />Transportability</td>
<td><a href="https://www.youtube.com/watch?v=JNq4oCV9C5k&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=77"
target="_blank" rel="noopener noreferrer">Video</a><br />
<a href="/slides/12%20-%20Transfer%20Learning%20and%20Transportability.pdf"
download="">Slides</a><br />
</td>
<td>Chapter 13 of ICI<br />(Coming Soon?)</td>
<td><a href="https://arxiv.org/abs/2006.07433" target="_blank" rel="noopener noreferrer">A
causal framework for distribution generalization (Christiansen et al., 2020)</a>
</td>
</tr>
<tr><td>13</td>
<td>Apr 27</td>
<td><a href="https://yoshuabengio.org/profile/" target="_blank"
rel="noopener noreferrer">Yoshua Bengio</a> <br />Causal Representation
Learning</td>
<td><a href="https://www.youtube.com/watch?v=rKZJ0TJWvTk&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=80"
target="_blank" rel="noopener noreferrer"></a><br />
<a href="/slides/Yoshua_Bengio_Guest_Talk_Towards_Causal_Representation_Learning.pdf"
download="">Slides</a><br />
</td>
<td>None</td>
<td><a href="https://arxiv.org/abs/1907.02893" target="_blank"
rel="noopener noreferrer">Invariant Risk Minimization (Arjovsky et al., 2019)</a>
</td>
</tr>
<tr><td>14</td>
<td>May 4</td>
<td>Counterfactuals<br />Mediation</td>
<td><a href="https://www.youtube.com/watch?v=f8PEpthLlN4&list=PLoazKTcS0Rzb6bb9L508cyJ1z-U9iWkA0&index=81"
target="_blank" rel="noopener noreferrer">Video</a><br />
<a href="/slides/14%20-%20Counterfactuals%20and%20Mediation.pdf"
download="">Slides</a><br />
</td>
<td>Chapter 14 of ICI<br>Coming Soon? </td>
<td><a href="https://ftp.cs.ucla.edu/pub/stat_ser/r321-ijcai05.pdf" target="_blank"
rel="noopener noreferrer">Identifiability of Path-Specific Effects (Avin, Shpitser,
& Pearl, 2005)</a></td>
</tr>
</tbody>
</table>
<h2 id="course-textbook">Course Textbook</h2>
<p>Draft of first 10 chapters (continually updated with new chapters throughout the course):</p>
<div style="text-align: center;">
<a href="Introduction_to_Causal_Inference-Dec17_2020-Neal.pdf">
<span style="font-size: 24px;">Introduction to Causal Inference (ICI)</span><br />
<span style="font-size: 18px;">from a Machine Learning Perspective</span>
</a>
</div>
<p>This is a book <em>draft</em>, so I greatly appreciate any feedback you're willing to send my way.
If you're unsure whether I'll be receptive to it or not, don't be.
Please send any feedback to me using the “Book� option of the <a
href="https://docs.google.com/forms/d/e/1FAIpQLSfoDk_PftCTD5aSqz7TP_MG8heIw0wSH4OVEkIsSvCaLgSsXw/viewform?usp=sf_link">feedback
form</a>.
Feedback can be at the word level, sentence level, section level, chapter level, etc.
Here's a non-exhaustive list of useful kinds of feedback:</p>
<ul>
<li>Typoz.</li>
<li>Some part is confusing.</li>
<li>You notice your mind start to wonder or don't feel motivated to read some part.</li>
<li>Some part seems like it can be cut.</li>
<li>You feel strongly that some part absolutely should not be cut.</li>
<li>Some parts are not connected well.</li>
<li>When moving from one part to the next, you notice that there isn't a natural flow.</li>
<li>A new active reading exercise you thought of.</li>
</ul>
<h2 id="prerequisites">Prerequisites</h2>
<p><strong>There is one main prerequisite: basic probability.</strong> This course assumes you've taken
an introduction to probability course at the undergraduate level or have had equivalent experience.
Topics from statistics and machine learning will pop up in the course from time to time, so some
familiarity with those will be helpful, but is not necessary.
For example, if cross-validation is a new concept to you, you can learn it relatively quickly at the
point in the course that it pops up.
And in Section 2.4 of the book, we give a primer on some statistics terminology that we'll use.</p>
<h2 id="faqs">FAQs</h2>
<p>Q: Where should I ask questions about a given lecture?<br />
A: Use the YouTube comment selection below the relevant video. I check it once per day on week days.
</p>
<p>Q: Is this course for credit?<br />
A: No.</p>
<p>Q: Is this course free?<br />
A: Yes!</p>
<p>Q: What time is the course?<br />
A: Only the guest talks will have specific times (listed in the schedule). The regular lecture
videos won't be live and will usually be uploaded to YouTube on Mondays.</p>
<p>Q: I'm not receiving course emails.<br />
A: Email me with “[Causal Course]� at the beginning of your email subject, and I'll fix it.</p>
<h2 id="feedback">Feedback</h2>
<p>If you have any feedback about the course to send my way, I welcome it!
Please send it <a
href="https://docs.google.com/forms/d/e/1FAIpQLSfoDk_PftCTD5aSqz7TP_MG8heIw0wSH4OVEkIsSvCaLgSsXw/viewform?usp=sf_link">here</a>.
You can include your name or not include your name.
Either works.</p>
<h2 id="potential-reading-group-papers-by-week">Potential Reading Group Papers by Week</h2>
<p>We will have a small weekly reading group that runs in parallel to the course.
Before any given week's reading group meeting, 1-3 people will have read the week's paper in detail
and already thought about discussion topics.
These 1-3 people will then lead a discussion of a small number of people who have all made
themselves familiar with the paper.
The discussion group will be kept small (at most 15) in order to facilitate quality discussion.
You can ensure that you have a place in the discussion group every week you'd like by signing up to
be a discussion leader for at least one week.
Below, I give a list of potential reading group papers, organized by week/topic, just like the
course schedule is.
You can email me at bradyneal11@gmail.com to let me know that you'd like to lead a certain week's
discussion, which paper(s) you're considering, or to discuss other papers you'd like to discuss that
are not on the list.</p>
<ol>
<li>Motivation and Preview - No reading group</li>
<li>Potential Outcomes
<ul>
<li><a href="https://www.nature.com/articles/ijo200882">Does obesity shorten life? The
importance of well-defined interventions to answer causal questions (Hernán &
Taubman, 2008)</a></li>
<li><a href="https://ftp.cs.ucla.edu/pub/stat_ser/r483-reprint.pdf">Does Obesity Shorten
Life? Or is it the Soda? On Non-manipulable Causes (Pearl, 2018)</a></li>
</ul>
</li>
<li>Graphical Models and SCMs
<ul>
<li><a
href="https://www.degruyter.com/view/j/jci.2019.7.issue-1/jci-2019-2002/jci-2019-2002.xml">On
the Interpretation of do(x) (Pearl, 2019)</a></li>
<li><a href="https://arxiv.org/abs/1203.6502">Quantifying causal influences (Janzing et al.,
2012)</a></li>
<li><a href="https://ftp.cs.ucla.edu/pub/stat_ser/r391.pdf">Trygve Haavelmo and the
Emergence of Causal Calculus (Pearl, 2014)</a></li>
</ul>
</li>
<li>Randomized Experiments, Frontdoor Adjustment, and <em>do</em>-calculus
<ul>
<li><a
href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.644.1881&rep=rep1&type=pdf">Single
World Intervention Graphs: A Primer (Richardson & Robins, 2013)</a></li>
</ul>
<ul>
<li><a
href="http://marcfbellemare.com/wordpress/wp-content/uploads/2019/08/BellemareBloemFDCAugust2019.pdf">The
Paper of How: Estimating Treatment Effects Using the Front-Door Criterion (Bellemare
& Bloem, 2019)</a></li>
<li><a href="https://causalai.net/r60.pdf">On Pearl's Hierarchy and the Foundations of
Causal Inference (Bareinboim et al., 2020)</a></li>
</ul>
</li>
<li>Estimation and Conditional Average Treatment Effects
<ul>
<li><a href="https://arxiv.org/abs/1606.03976">Estimating individual treatment effect:
generalization bounds and algorithms (Shalit, Johansson, & Sontag, 2017)</a>
</li>
<li><a href="https://arxiv.org/abs/1906.02120">Adapting Neural Networks for the Estimation
of Treatment Effects (Shi, Blei, Veitch, 2019)</a></li>
<li><a href="https://arxiv.org/abs/1610.01271">Generalized Random Forests (Athey,
Tibshirani, Wager, 2019)</a></li>
<li><a href="https://arxiv.org/abs/1706.03461">Meta-learners for Estimating Heterogeneous
Treatment Effects using Machine Learning (Künzel et al., 2017)</a> (caution: not
about meta-learning in the ML sense)</li>
</ul>
</li>
<li>Sensitivity Analysis
<ul>
<li><a href="https://rss.onlinelibrary.wiley.com/doi/full/10.1111/rssb.12348">Making sense
of sensitivity: extending omitted variable bias (Cinelli & Hazlett, 2019)</a>
</li>
<li><a href="https://arxiv.org/abs/2003.01747">Sense and Sensitivity Analysis: Simple
Post-Hoc Analysis of Bias Due to Unobserved Confounding (Veitch & Zaveri,
2020)</a></li>
<li><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3800481/">An Introduction to
Sensitivity Analysis for Unobserved Confounding in Non-Experimental Prevention
Research (Liu, Kuramoto, & Stuart, 2013)</a></li>
<li><a href="http://proceedings.mlr.press/v97/cinelli19a.html">Sensitivity Analysis of
Linear Structural Causal Models (Cinelli et al., 2019)</a></li>
</ul>
</li>
<li>Instrumental Variables, Regression Discontinuity, Difference-in-Differences, and Synthetic
Control
<ul>
<li><a
href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.883.6034&rep=rep1&type=pdf">Improving
Causal Inference: Strengths and Limitations of Natural Experiments (Dunning,
2007)</a></li>
<li><a href="http://paa2019.populationassociation.org/uploads/190202">Alternative Causal
Inference Methods in Population Health Research: Evaluating Tradeoffs and
Triangulating Evidence (Mattay et al., 2019)</a></li>
<li><a href="http://proceedings.mlr.press/v70/hartford17a/hartford17a.pdf">Deep IV: A
Flexible Approach for Counterfactual Prediction (Hartford et al., 2017)</a></li>
<li><a href="https://www.princeton.edu/~davidlee/wp/RDDEconomics.pdf">Regression
Discontinuity Designs in Economics (Lee & Lemieux, 2010)</a></li>
<li>Synthetic Controls (there are several different Abadie papers; message me, if you're
interested in this topic)</li>
</ul>
</li>
<li>Causal Discovery without Experiments
<ul>
<li><a href="https://www.nature.com/articles/s41467-019-10105-3">Inferring causation from
time series in Earth system sciences (Runge et al., 2019)</a></li>
<li><a href="https://jmlr.org/papers/v17/14-518.html">Distinguishing Cause from Effect Using
Observational Data: Methods and Benchmarks (Mooij et al., 2016)</a></li>
</ul>
<ul>
<li><a
href="https://www.cs.helsinki.fi/u/mjarvisa/papers/hyttinen-eberhardt-jarvisalo.uai15.pdf">Do-calculus
when the True Graph Is Unknown (Hyttinen, Eberhardt, Jarvisalo, 2015)</a></li>
<li><a href="https://www.frontiersin.org/articles/10.3389/fgene.2019.00524/full">Review of
Causal Discovery Methods Based on Graphical Models (Glymour, Zhang, & Spirtes,
2019)</a></li>
<li><a href="https://rss.onlinelibrary.wiley.com/doi/10.1111/rssb.12167">Causal inference by
using invariant prediction: identification and confidence intervals (Peters,
Bühlmann & Meinshausen, 2016)</a></li>
<li><a
href="https://papers.nips.cc/paper/3548-nonlinear-causal-discovery-with-additive-noise-models.pdf">Nonlinear
causal discovery with additive noise models (Hoyer et al., 2008)</a></li>
<li><a href="https://arxiv.org/abs/1903.01672">Causal Discovery from
Heterogeneous/Nonstationary Data with Independent Changes (Huang et al., 2020)</a>
</li>
</ul>
</li>
<li>Causal Discovery with Experiments
<ul>
<li><a href="https://jmlr.csail.mit.edu/papers/v14/hyttinen13a.html">Experiment Selection
for Causal Discovery (Hyttinen, Eberhardt, Hoyer, 2013)</a></li>
<li><a href="https://arxiv.org/abs/1104.2808">Characterization and Greedy Learning of
Interventional Markov Equivalence Classes of Directed Acyclic Graphs (Hauser &
Bühlmann, 2012)</a></li>
<li><a href="https://arxiv.org/abs/1802.06310">Characterizing and Learning Equivalence
Classes of Causal DAGs under Interventions (Yang, Katcoff, & Uhler, 2018)</a>
</li>
<li><a href="https://www.jmlr.org/papers/volume21/17-123/17-123.pdf">Joint Causal Inference
from Multiple Contexts (Mooij, Magliacane, & Claassen, 2020)</a></li>
</ul>
</li>
<li>Transportability and Transfer Learning
<ul>
<li><a href="https://ftp.cs.ucla.edu/pub/stat_ser/r400-reprint.pdf">External Validity: From
Do-Calculus to Transportability Across Populations (Pearl & Bareinboim,
2014)</a></li>
<li><a href="https://arxiv.org/abs/2006.07433">A causal framework for distribution
generalization (Christiansen et al., 2020)</a></li>
<li><a href="https://www.pnas.org/content/113/27/7345">Causal inference and the data-fusion
problem (Bareinboim & Pearl, 2016)</a></li>
<li><a href="https://icml.cc/2012/papers/625.pdf">On Causal and Anticausal Learning
(Schölkopf et al., 2012)</a></li>
<li><a href="http://proceedings.mlr.press/v28/zhang13d.html">Domain Adaptation under Target
and Conditional Shift (Zhang et al., 2013)</a></li>
<li><a href="https://mingming-gong.github.io/papers/AAAI_MULTI.pdf">Multi-Source Domain
Adaptation: A Causal View (Zhang, Gong, & Schölkopf., 2015)</a></li>
<li><a href="http://www.jmlr.org/papers/volume19/16-432/16-432.pdf">Invariant Models for
Causal Transfer Learning (Rojas-Carulla et al., 2016)</a></li>
<li><a href="https://arxiv.org/abs/2002.03278">Domain Adaptation As a Problem of Inference
on Graphical Models (Zhang et al., 2020)</a></li>
<li><a href="https://arxiv.org/abs/1707.06422">Domain Adaptation by Using Causal Inference
to Predict Invariant Conditional Distributions (Magliacane et al., 2018)</a></li>
</ul>
</li>
<li>Counterfactuals, Mediation, and Path-Specific Effects
<ul>
<li><a href="https://imai.fas.harvard.edu/research/files/mediation.pdf">Identification,
Inference and Sensitivity Analysis for Causal Mediation Effects (Imai, Keele, &
Yamamoto, 2010)</a></li>
<li><a href="https://ftp.cs.ucla.edu/pub/stat_ser/r321-ijcai05.pdf">Identifiability of
Path-Specific Effects (Avin, Shpitser, & Pearl, 2005)</a></li>
<li><a href="https://ftp.cs.ucla.edu/pub/stat_ser/r389.pdf">Interpretation and
Identification of Causal Mediation (Pearl, 2014)</a></li>
</ul>
</li>
<li>TBD - Overflow Week</li>
<li>Causal Representation Learning
<ul>
<li><a href="http://www.its.caltech.edu/~fehardt/papers/CPE_UAI2015.pdf">Visual Causal
Feature Learning (Chalupka, Perona, & Eberhardt, 2015)</a></li>
<li><a href="https://arxiv.org/abs/1605.08179">Discovering causal signals in images
(Lopez-Paz et al., 2017)</a></li>
<li><a href="https://arxiv.org/abs/1907.02893">Invariant Risk Minimization (Arjovsky et al.,
2019)</a></li>
</ul>
</li>
</ol>
</div>
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A repository for the NYUSH graduate seminar based on Brady Neal's Causal Inference Course.
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