Course Details

Pros: 1. Causal inference is an interesting topic. 2. Not a large time commitment. Cons: 1. The course was very philosophical. 2. HW and exam questions are ambiguous and frustrating. Detailed Review: Overall, this has been my least favorite course that I have taken in the program (out of 7 completed). The topic of Causal Inference is useful and interesting, but unfortunately, the course was taught more like a philosophy course. This led to ambiguous and confusing questions on both the homework and exams. Often, the answers to HW questions and exam questions were wrong, and the grading had to be updated after multiple student complaints on Ed Discussion.

Pros: 1. Really interesting material 2. Great lectures and textbook Cons: 1. Difficult homework and exams 2. Some of the homeworks needed some QA/QC before being released Detailed Review: This was probably my favorite course on the stats side of this program. The material was really interesting, and I felt it did a really good job covering how to properly view data analysis from a casual lens. The professor lectures were great and the textbook was very approachable and not written at a too academic level. The homeworks and exams were both pretty tough, definitely the toughest on the stats side of this program. Many of the questions relied on using the course knowledge and applying it to situations that are not very intuitive how one would apply the casual framework. On the exams you are given 24 hours and 48 hours respectively, so fortunately time doesn't play a factor on them. There were a couple homeworks with some errors in the solution key, which lead to effectively none the answers being truly correct. The course staff went back and corrected them after the due date, but it definitely made these couple of assignments a much more frustrating experience than necessary. The quizzes were changed to completion grades. They still ended up being useful as study material for the exams though.

Detailed Review: While it is apparent there were updates made to improve this class from prior iterations (mainly making peer graded "quizzes" into completion grades), but there is still a lot that can be improved in this course. The lectures themselves are overall pretty good, sometimes sparse in the practical applications necessary to handle the later assignments in the course, but usually a good starting point to understanding the material in conjunction with the textbook (which is mandatory to read only if you want to get perfect scores on the homeworks). There were some short coding examples given, but I found that reading the documentation directly was more helpful after clarifying the terminology from lecture. Homeworks are a mix of multiple choice and select all that apply which are immediately machine graded upon the due date. Several of the assignments later in the class had major errors that led to confusion before the due date and eventual regrades, and the course staff did little proactively to address these situations. In general, the only helpful discource on Ed was from other classmates, with the 2 TA's responses directly contradicting each other at times. The select all that apply questions are called out often as a pain point in this class, but honestly they're typically worth so few points and have the potential for partial credit that they're not so bad. In my opinion, the numerical multiple choice questions with "choose the closest answer" were much more frustrating, as often the closest answer would be ambiguous between two choices or far from every choice somehow. This was also the major source of errors in the assignments, so hopefully it is addressed in future iterations, but I wouldn't get my hopes up. Quizzes, as mentioned before, are completion grades and mostly feel like busy work. The questions were vague and the rubric specific to the point where the majority of peers that I graded (and myself) would have gotten an awful grade if it weren't completion. They're mostly just a grade boost and a time sink, your focus should go much more to the homeworks and exams. A generous window of time is given for the midterm and final, but know that these exams are very involved and will take more than "a couple of hours" as stated by the TAs. The exam questions were a step up in complexity from the homework, and often featured completely new concepts that you'd have to use to answer the question. If you feel that you have a good conceptual grasp of the material, there's likely little additional to gain from studying the material in depth. A qualm I had with both exams and homeworks in this class was the imbalance in point values between questions. It felt very unoptimized, where I would have to read a significant amount of a journal article to answer a one point question vs. answering a true/false question for another point somewhere else in the assignment. As this course is graded on an unspecified curve, this drove me to play the numbers game instead of aiming for perfection, which is definitely not worth trying to attain in this class with the number of unclear and ambiguous questions and answers. Overall, I feel like this course was interesting and introduces a mindset that is novel to most and useful for causal inference, but the struggle of getting through this course in general detracts from my personal interest in learning about the frontiers of causal inference. Also, I continue to wonder how relevant this material is outside of controlled research settings. I could not imagine applying most of what we learned about in this course to my work, which is unfortunate because causality is something that is loosely thrown around.

Pros: 1. Pretty good textbook 2. Lectures are a good mix of theory and example 3. Exposure to a different way of thinking Cons: 1. Major errors on virtually every homework/exam 2. TAs are not helpful - they take days to respond and almost never answer questions directly 3. HW/exam questions feel more like riddles than clear problem statements Detailed Review: I'm going to be honest: by the end of the semester, I put about as much effort into this class as the course staff. Which is to say I got through it as quickly as possible and stopped worrying about the results. Basically every single week, the professor or the TAs post a regrade notice in Ed because there was some error that made it impossible to correctly answer a homework question or two (or more). Sometimes they posted the wrong data; sometimes they didn't include the correct answer choice; sometimes the questions don't make grammatical sense. While I appreciate that they go back and regrade, the fact that this happens every single week, and even on exams, is ludicrous. This isn't the first time this course has been offered, or even the second. But no one seems to go back and check to make sure they aren't making the same mistakes over and over. Meanwhile, when you try to clarify mistakes, it's almost impossible to get an answer from the TAs. If they respond at all, it'll probably be a non-specific answer (even to a yes-no question) days or even a week later. Even during exam periods, they don't give prompt responses (for example - 30 hours to respond about a grammatically nonsensical question on the final, and even then they wouldn't tell me what the question meant to ask). So you're just left guessing what you're supposed to do with internally inconsistent data or nonsensical questions. Meanwhile, the problems build on one another, so a mistake in an early part means you have no chance of getting points later. This is unfortunate, because I do think the material is interesting. Prof. Zigler is a decent lecturer, and his material does a good job of complementing the book (which is actually worthwhile to read). The class focuses on a single framework (potential outcomes), so you shouldn't go in expecting that you're going to get lots of different perspectives. But the course does a good job of explaining the interplay between design and analysis (though it takes a little while to get to the point). But unfortunately, this can only go so far when, on most assignments, you have no idea if your approach is wrong or if the question simply cannot be answered as written, nor can you get any clarification. This isn't conducive to learning the material at any level, and makes this little different from just watching free lectures on Youtube or your favorite MOOC site. I still think this class makes sense in the program, but I wouldn't recommend it in its current, unpolished state. One change from previous semesters is that you get full credit on the short-answer quizzes as long as you answer the questions and complete peer grading. You can still see feedback on the (very specific) answer criteria, but the actual peer grading points don't matter on your final grade. All regular homework and exams are machine-graded multiple choice.

Pros: 1. Learn important information about how to design randomized experiments and understand observational studies 2. Generous grading curve at the end 3. Some coding in R required, but again not terribly difficult Cons: 1. Grading drives you crazy 2. Homework assignments within the same week were the most difficult Detailed Review: Very mixed views on this class. On one hand, the content is actually very useful to understand and be able to relate to real-world studies. The main con is that the assignment towards the last 1/3 of the course are long and tedious. Additionally, the grading drives you crazy. Questions will have several different options (choose all that apply) and can be very subjective, or differ by the slightest wording. Solutions are often very vague. The final exam is also design to be have some trick questions. Fortunately, the curve applied is fairly generous, so with some solid effort and patience a reasonable grade is very possible.

Pros: 1. Applicable and interesting material 2. Strong potential to alter your (critical) thinking skills 3. You will learn Cons: 1. Official solutions rarely provided 2. Piazza discussions are limited 3. YOUR final grade depends on others' performances Detailed Review: This review is mainly to provide an updated review. Many of the earlier reviews still apply. Regression is listed as a pre-req. I took it concurrently with this class; it is doable if you are willing to learn a couple regression topics on your own. The main tenets of regression are taught before they are needed for this class. The material is interesting. It will very clearly apply to data science. It will get you to question assumptions about your own thinking and others - which is valuable. Lectures are straightforward. Concepts are not difficult to understand. Some assignments/questions do seem to make you guess what Dr. Zigler is thinking or meaning which yields ostensible multiple "correct" answers. Often the answer choices have very subtle differences. Sometimes this leads you to get zero points for a question that you narrowed down to two choices because you knew 95% of the correct reasoning. This was the most frustrating aspect of the course. Review lecture notes carefully, even when you don't think you need to. Official answers are not typically provided. This unfortunately can result in us not learning whatever it was we were supposed to learn - another, but smaller, point of frustration. Navigating this course's culture on how much discussion with other students would be tolerated was challenging. (Refer to the syllabus and every pre-assignment "no cheating" attestation). Piazza discussions were limited in number from other students and instructors. Help from others was rare for our class but picked up noticeably in the final few weeks. This was in clear contrast to the other 3 courses I've taken where people seem willing to help all semester. Knowing one's own grade depends on how well or poorly others do can bias how much people are willing to help and how they grade your quizzes. If requested, the TA was willing to regrade your quiz with a good regrading policy. In contrast to some other reviews, the TA (for summer 2022, Bernardo) was helpful during office hours. Grading: (similar to other reviews) Grades are based on the distribution of everyone's grades. This is both good and bad. It can be unnerving not knowing how you are doing in the class grade-wise. While the instructors do typically provide the distributions of each assignment, the means vary. Overall, it's a bittersweet course - It was equally stimulating and frustrating.

Pros: 1. 2. 3. Cons: 1. The material was vague, the explanation was vague, 2. TAs were of no use, the most answer they could give during office hours: "umm, I can't say more or I will give away the answer' or 'umm, yeah' or 'let me think how can I answer without giving you too much information'. 3. Professor sent email of penalizing students while the final exam window was open! Detailed Review: The course was very vague, material was not well explained. The quizzes and the exams became 'what Zigler thinks' not what seems appropriate to majority of the class. TAs were very useless. I have taken 7 courses and I found this course had the worst TAs. Professor announced right during the final exam that he will penalize some students as they could not grade the last assignment 'as per his standards'. This is very unethical that you decided to bring down the moral of some students while they were preparing for the 35% grade for the class- the final! He should have sent that email after the final, not during it. The quizzes answers given were one liner or two liners. And sometimes he will say you have to have the exact word to get the grade just explaining will not give you full credit. The worst structed course in my experience so far.

Pros: 1. Lectures clear and engaging 2. Material applicable to almost anyone designing or assessing studies that try to prove that something causes something else 3. Assignments were useful to generate deeper understanding of material Cons: 1. Answers to assignments often depended on some obscure detail or were debatably correct. This impacted grades and was a general nuisance 2. R virtually required for some assignments (I couldn't muddle through in Python, though I tried, and my Python's pretty good) 3. Peer grading Detailed Review: Lectures were engaging. I felt I learned a lot and actually understood the material and how it could be relevant in the real world (unlike, say, the regression course). The curve in the end was quite generous - I got an A with an 87% (not sure where the actual cut-off for A/A- ended up being). Would have saved me a lot of heartburn knowing that curve from the beginning. As others have notes, the answers to the graded assignments were nitpicky, debatable, and sometimes incorrect. In many cases, credit was given after the fact for some problems. To be honest, I *liked* thinking about the little details and how they could impact the validity of the study. But the fact that the answers were binary (you're either right or wrong) stinks. For these sorts of problems, I'd rather have free response where I could explain my thinking and have the professor grade me based on how I thought things through. But with the staff:student ratio, that's just not possible. You get what you pay for at $1000/class. Overall though, I really liked the class. I think future students will too if they can avoid fretting too much about their grades. Oh, and you can probably just barely get away without buying the textbook and rely on your fellow students copying necessary extracts into Piazza. I bought it, but if your money is super tight, you might choose this route.

Pros: 1. Material was interesting and applicable 2. The lectures were clear 3. I learned a lot Cons: 1. The homework was a nightmare 2. The quizzes were a nightmare 3. The tests were a... you guessed it... nightmare Detailed Review: You learn some cool things in this class. I am more confident in my ability to read and synthesize observational studies and random experiments. I think I could run one on my own. Grades aside, I promise you will learn a ton. Grading could not have been any more frustrating than it was. Questions that are "choose all that apply" and have SEVEN OPTIONS. Multiple questions in a row where if you screw up the first then the subsequent are all wrong regardless if you did them right because its all computer graded so no partial credit. Exam questions that have choose the answer that is most correct styles with five options and the answers are all extremely similar. A/B/C lines are based purely on distribution and quizzes are peer graded so students have an incentive to tank your quiz grade (and believe me, they will). TAs will help you with material but its not like other classes where they will guide you towards correct answers on HW in TA sessions. You will get frustrated and annoyed at what turns out to be the answer choice. TAs will defend them to the death. You'll learn a lot, get a B, and move on with your life.

Pros: 1. Course staff was extremely responsive to queries on official channels. 2. All of the resources provided were great and addressed almost anything that could be asked; all content was available from day 1 with the exception of exams that could only be completed within certain windows. 3. During the first run of the course, it seemed like the instructional staff were open to feedback for modifications to future runs of the course. Cons: 1. Uncertainty of the grading scale was a cause for concern in some students; we found out the grading scale after the course was complete. Do the best you can on everything and hope that it's enough. 2. With the quizzes and homeworks having the same due dates at times, it made it a challenge to adjust my understanding of content to perform well on some quizzes. 3. There was at least one week where the amount of reading numbered in excess of 100 pages. This was on top of lectures and any other life events you might have going at the time. Detailed Review: Let me start this detailed review by stating that my average grade on all of the completed assignments as a 72. This resulted in an assigned grade of B-. My background includes course work that lead to a BS in Mathematics with a minor in Electrical Engineering from a public US university. Prior to this course, I have successfully completed DSC 381 PRB, DSC 382 RGR, and DSC 391L ML. While taking this course, I was also working 40+ hours a week and taking Optimization. I am married but without kids, so family obligations existed but were minimal compared to those with kids. The course is broken up into seven parts: (1) Intro to Causal Inference; (2) Randomized Studies; (3) Causal Inference in Regression; (4) Observational Studies; (5) Analyzing Observational Studies; (6) Quasi-Experimental Methods; and (7) Next Frontiers in Causal Inference. The first five parts have anywhere from two to three homework assignments and a culminating quiz. The sixth part only have a homework assignment, and the seventh part wasn't assessed. Part (1) of the course was a bit of a shock to me as it seemed to have a more subjective take on things whereas I had been used to all of my coursework in undergrad and grad being almost 100% objective. Pay careful attention to the wording of things in lecture and on assignments, and you should be fine. Part (2) of the course introduced a lot of new vocabulary and notation that carries on throughout the rest of the course, so take note of it and make sure to grasp the big ideas. This brought me to the first midterm. We were given 24 hours to complete the open note midterm from the time we hit "Begin". Ultimately, if you didn't understand the concepts, infinite time wouldn't have helped. Part (3) should be simple if you understood the content from DSC 382 RGR or some other experience with regression. That said, I didn't allocate enough time to complete all of the assignments for this part. I should have started immediately after I completed the midterm but gave myself a break since nothing was due for two weeks. Bad choice. Stay on top of the learning and don't set your pacing based solely on due dates. Parts (4) and (5) contain a lot of information, so be prepared to ask follow-up questions to truly master the content. I didn't make time for this, and it showed on my homework and quizzes. The need to use R (or Python) picked up by quite a bit in these parts, and my lack of experience in R necessitated a bit more time to score what I did. Part (6) rounded out the content that was primarily tested on the final exam. It wasn't necessarily difficult, but again, you need to make time to process all of this great information. Overall, I'm confident that I could not walk onto a job tomorrow and implement these ideas with a high degree of success; however, I do think that I have a strong foundation to build upon and could successfully implement them within 6 months after having enough time to process everything. I think it would be a good/interesting addition to the course to have Dr. Zigler take a set of new data and allow us to observe him go through the process of analyzing it from start to finish, making connections between what he's doing and what we learned in the course. It's possible that video could be too long for most, but I think the quality of such a video could be above all else.

Pros: 1. Develops a "scientific literacy" needed to interpret causal results in the real world. 2. Covers a variety of foundational and modern writings on causal inference. 3. Solid textbook, pretty good lectures. Cons: 1. Assessments 2. Peer grading 3. Detailed Review: This course was probably the most divisive in the program, but personally I loved it. Experimental design and proper interpretation of regression results is hardly ever given the attention it deserves, so I appreciate that this course is in our program. The course is more conceptual than technical, and as a practicing data scientist I've probably gained more from this class than the others I've taken so far. This class alone convinced me that I made the right decision to choose UT Austin over Georgia Tech's OMSCS. Lectures are mostly clear, and I especially appreciated the ones where Prof. Zigler walked us through some difficult to navigate papers. TAs were exceptional, and overall I thought communication between course staff and students was solid. Workload was just right imo, and I found the optional extra readings rewarding. The biggest gripe with this class is the assessments. They definitely forced me to engage closely with the material, but the score I received for some assignments/quizzes didn't reflect the effort I put in. I got an A in the class, so I'm not too salty about it, but the course would be greatly improved if assessments could move beyond multiple choice/ choose all that apply. Peer grading for the quizzes was kind of a mess, as many students were just not equipped to assign a grade to open ended free response questions. Unfortunately, these issues are hard to fix given the large course size. Ideally a class like this would be best suited as a small-sized course with plenty of discussion/free response.

Pros: 1. Material 2. 3. Cons: 1. Staff 2. 3. Detailed Review Literally, it was all about guessing what Zigler's intentions or assumptions behind questions in assignments or exams. Lectures were basically paraphrasing of the textbook and some articles. Regardless of how good and relevant the material is, this isn't a way to measure performance. It's like a matter of instructor's ego maybe rather than the understanding of students (really unclear and they were not willing to clarify the answers in some cases). In another course, we had similar situations where more than one answer made sense under different assumption, and professor happily acknowledged the confusion and considered the other approach correct. For what it matters, I'm getting A in the class; nevertheless, I wouldn't recommend this class.

Pros: 1. Fantastic area of study 2. Extremely applicable 3. Very responsive TA's. High engagement in office hours meant there was a lot to be learned there. Cons: 1. Assignments could have done with some quality control. Answers were often corrected after student challenges. 2. This course could incorporate an analysis project but doesn't. 3. Detailed Review: First off, I loved this class. In my career, I've done a lot of statistics and prediction based on association (as I recognize now), and looking back now I can see I missed some opportunities for projects that would have had seriously interesting outcomes. For the benefit of future students, I was in a few different groups and saw that some people struggled with a couple of things in this course. First, most of the math was very simple, but you do need to know how to do regression. I saw a small few people struggle with that, and getting frustrated because they spent more time trying to learn how to do a regression than they did learning the actual material. Second, I saw a small few people with basic Python skills refuse to try R until late in the course, attempting instead to learn Python by doing the assignments with it. I would have preferred to use Python myself, and it's arguably more valuable in the workplace, but it's not a good idea to try and learn it in a course where you're trying to learn the things that are explained with R code. Trying to persevere in Python (unless you're already very comfortable with it), you'll be on the back foot all semester, and in the end the scripting in the class is fairly basic so I don't think it really matters. Finally, I think a small few people didn't spend enough time on the readings. They can be a bit much but they are foundational and once you read them a lot of things in the course start to become obvious. Not everything in data science revolves around creating more cutting-edge machine learning algorithms - to a large extent anyone can plug numbers into models. To do well in any technical career you need to be more than a technician, and this course provides some excellent strategies and approaches to help you move beyond that.

Pros: 1. None Cons: 1. Confusing course material 2. TAs tried but were generally unhelpful 3. It's the Zigler show! Detailed Review: I really really hated this course all around. The course material started out interesting, but it quickly became clear that much of what Zigler is teaching is either outdated (1960-70s papers), uncommon (used terms that no one else uses ever) or just wrong. He also chose many articles that were extremely poorly written as examples of certain concepts. It quickly became clear when reading these articles that the question I should be asking is not "what are the authors trying to say?" but "what does Zigler think the article is trying to say?" This made the homework assignments insanely frustrating because even when I thought I had a solid understanding of something while watching lectures and reading the text, I would still get things wrong. Furthermore, there was little guidance on the homework questions that required some statistical analysis in R. We were given some code that was supposed to help us follow the lecture demonstrations, but it was poorly written and very hard to follow. We were left to our own devices for the most part because TAs didn't want to "give away too much." The lectures themselves were not that bad, but they were not very well organized. Additionally, they often did not cover all of the materials necessary to help you understand the assignment for that week. A lot of the coding information came from external sources. I couldn't find good information/better examples on certain conceptual concepts online because the terms are used only by Zigler and like 2 other scientists from the 1900s. There were also a few times that Zigler said something in Piazza that seemed to contradict something that was said in his own lecture. The first exam scores disappointed a lot of students because many of the "theoretical" questions seemed to have at least 2 right answers and you were down to blind luck to pick the right ones. While one of the TAs tried very hard throughout the semester to help students with their understanding, it seemed that her primary function was to support Zigler's answers and (sometimes flawed) reasoning for the "correct" answer choices. The amount of mental gymnastics that was required to explain the nuances between almost identical answer choices was astounding. The other TA was much less active and honestly, for a while, I forgot that we had more than 1 TA. I attended his office hours a few times in the beginning, but found his answers to be more detrimental to my understanding than anything else. On more than one occasion, his answers to a pointed question about a concept on the homework made me switch my answer from the correct one to the WRONG one. Hm. Lastly, the 'quizzes' were not very informative. It was difficult to figure out what the questions were asking a lot of the time, which made it impossible to guess exactly what would score points during the peer grading. I also found that the peer grading was not always very fair or helpful. On many occasions, a student would give me 0 points for a section without explaining themself. Edx also only took the first ~3 grades into consideration when giving you a score, so sometimes the true median of all of your scores (usually 5-8 grades) would be different. Depending on which 3 students graded your assignment first sometimes put your grade down to luck. We then had to email the TAs/instructor to give points back. I realize this is more a limitation of Edx, but it made it no less frustrating because I also feel like double checking grades is not very time consuming and a responsibility of anyone running a course. I truly hope that this course gets better over time. There was a lot of (constructive) criticism about wording on just about every homework assignment and the midterm, and Zigler seemed to be pretty open about incorporating those changes when he wasn't being elusive. While I cannot say that this course was difficult because the course material itself was difficult, it became difficult due to the poorly explained concepts, confusing wording on assignments/tests and general lack of support.

Pros: 1. Lots of interesting information 2. TAs were relatively helpful with confusing questions Cons: 1. HWs are a big leap from the lectures sometimes and very confusing 2. Can be very difficult if you haven't had regression experience 3. Peer graded quizes are unclear sometimes Detailed Review: