Lukhanyo Nyati

Public Health Researcher, Data Scientist, Passionate about Science Information for Public, Passionate about Youth Development

[Lukhanyo Nyati @numbers_doc]

I've been waiting for a call from sales people for the @SizweHosmed just rewards for the past 2 days. Even sent an email query and haven't heard back. Not a good sales pitch for your programme.

[Lukhanyo Nyati @numbers_doc]

Also interesting to see how they performed over time. (Update to follow after final count)

[Lukhanyo Nyati @numbers_doc]

Interesting to see how parties that broke away from the ANC post 1994 performed in their inaugural national elections. MK, by far, outstripping the rest. (Update to follow after final count)

[Lukhanyo Nyati @numbers_doc]

@DrCathD Thanks, Cathi, for capturing the conversations so well in the manuscript!

[Lukhanyo Nyati @numbers_doc]

@WCLiveTraffic @LimaCharlie1 Is this route still closed for one traveling to CT?

[Kareem Carr, Ph.D. @kareem_carr]

What is Causal Inference?

Causal Inference is a new science of causation. This field is nothing less than a revolution in how scientists understand data. Read on to learn more.

This is the first post in a series based on the Book of Why by Judea Pearl. I will be reading the book and sharing the big insights with my followers.

When I first started learning causal inference, I didn't have a clear idea of the problems that casual inference was trying to solve. My misconceptions made it harder to understand the material than it would have been otherwise.

So, before we get into the ideas of the book, I want to help you avoid these common misconceptions.

1. Causal Inference is NOT just regular science

All sciences strive to infer causes within their domain of expertise. Therefore, it might not be obvious to you what makes casual inference any different. This is the reason why I sometimes call this new field mathematical causal inference. This term emphasizes that what sets causal inference apart is the mathematical framework it uses to describe causation.

2. Causal Inference is NOT directly about inferring causes

Based on the name, new learners often think causal inference is solving the following problem:

Given a list of candidate variables, how can we select the ones that have a real causal effect on our outcome of interest?

This is not what causal inference does. Causal inference is solving a different problem:

Assuming our beliefs about the causal relationships between all the variables is accurate, what is the best estimate of the causal relationship between a particular candidate variable and the outcome of interest?

Very roughly speaking, causal inference tells us whether based on our causal beliefs, the association between two variables is bigger or smaller than their true casual relationship.

3. The Example of Alice and Bob

Alice thinks genes strongly affect addictive behaviors like smoking. She also thinks genes have an effect on who gets cancer. Bob agrees that genes very likely have an effect on cancer, but Bob thinks complicated social behaviors like addiction are completely due to social factors, not genes.

Causal inference allows us to evaluate the same data according to both Alice's and Bob's beliefs about the underlying causal relationships. This allows for various outcomes:

1. Avoiding unnecessary arguments. If Alice and Bob get very similar estimates for the causal relationship between genes and cancer, this implies that the disagreement about the relationship between genes and behavior is not that important. This allows scientists to move forward by focusing on the factors that really matter.

2. Agreeing to disagree. If the difference in estimates of the casual relationship between genes and cancer is large, causal inference allows both Alice and Bob to continue to explore the same data according to their very different assumptions about the causal relationships. This gives scientists and policy makers autonomy to pursue different interpretations of the same data.

4. Casual Inference builds doesn't replace statistics. It makes it more powerful.

Causal inference allows us to adjust our statistical estimates of the strength of particular casual relationships based on our beliefs about the casual relationships between the variables. This is why some experts in causal inference (like the epidemiologist @epiellie) prefer to use the term causal effect estimation to refer to the field causal inference.

That's it for now. My next post (coming soon!) will explore how causal inference creates a mathematical model of causation and what makes this approach so special. (You can find these posts using the hashtag #KareemReads)

Follow me (@kareem_carr) for more content like this. If you want to show support, like and retweet the thread.

[Lukhanyo Nyati @numbers_doc]

Hi @Targus. I bought this bag between 2011-2013. Lost the receipts. The frame supporting the handle broke. Can you help?

[Kareem Carr, Ph.D. @kareem_carr]

Here are three different ways of thinking about linear regression and why it works.

[Bhekisisa @Bhekisisa_MG]

📺Fewer girls in Africa have the chance to go to school than boys. It makes it hard for them to find jobs as adults. Find out what this means for #HIV via @miamalan and @DylanBushZA. https://t.co/5LkxFiUK1x

[Lukhanyo Nyati @numbers_doc]

@DaniellaWatson_ @UoS_Medicine @unisouthampton @drchandnimjacob @McGrathCarmel93 @sshaw19 @Ravtaheem @BarkerSoton @DrWendy13 @SHKehoe @KateAWard17 Congratulations Daniella and everyone👏🏿

[Sarah J Tracy 🏜 @SarahJTracy]

Some folks assume that #qualitative research is always inductive & grounded—that magically, after repeated readings, the “themes emerge.”
As such, they drag their feet in reviewing literature & wait until the end before wading into focused analysis. The result?
🧵

[Lukhanyo Nyati @numbers_doc]

Every step counts! I've reached a psychological milestone in my academic journey. I've breached the 20-publications mark, 7 of those between January & July 2023. The 20th is very special! It's by my first PG student as a supervisor . She passed her Masters with a distinction.

[GCRO @GCR_Observatory]

CALL FOR APPLICATIONs from early career researchers to participate in a writing workshop on the theme 'Governing for urban inclusion' in April 2024. Read the full details and application process here: https://t.co/zQh4cFpqtY

[Lukhanyo Nyati @numbers_doc]

@CIatUCT @UCT_news @UCTHealthSci @UCT_Research Congratulations Wiedaad

[Lukhanyo Nyati @numbers_doc]

@JoburgWater potential burst water pipe a 11 Princess Place, Parktown.

[Lukhanyo Nyati @numbers_doc]

@Eytshpee Department of Social Development, ndicela nizomamela apha 🤭

[Lukhanyo Nyati @numbers_doc]

@Busisani_S Just heard your conversation on radio. Very interesting study. Interested in taking some discussion. What's the easiest way to contact you?

[Academic Exit @academic_exit]

PhDs working outside of academia: do you make more money in your post-academic role than you did in academia?

Inquiring minds (of PhDs still in academe) want to know.

[Anna Clemens, PhD @scientistswrite]

As an academic writing coach, here are the 7 mistakes I see supervisors and PIs make in the process of co-writing a paper with their PhD students or mentees.

A thread. 🧵

#AcademicChatter #newPI #PhDchat

[Birth to Thirty @bt30cohort]

Our Webinar Series kicks off on 13th September 2022 with a discussion between Prof Shane Norris and Dr. Lukhanyo Nyati - @numbers_doc- on "Growth and body composition across the lifecourse. Register here: https://t.co/2y72AxJcl6