Propensity Score Matching (2015)

[@abdullahbinabbas8965]

Lady did a better job in 5 minutes than my PhD professors in 2 weeks

[@amalfarooqi2379]

I was searching for some simple and clearly explained definition of psm and I found yours. It was really great , Thank you... and it felt very nice that you acknowledged your teachers and seniors for their help, a very rare practice. keep it up.

[@LauraTeAhoWhite]

Thankyou so much, this is the first video I've watched that explains what propensity score matching is without making me feel stupid.

[@chadbugg3416]

Thanks! So glad I found a video that went right to the point without dragging on. Wasn't boring or dull.

[@craiglowthorp6100]

After reading the Wikipedia entry on PSM, I *thought* this was what they were talking about, but your video with cards for confounding variables really helped me grasp this concept. Thank you so much!

[@BilalAhmed-jt5oe]

Thanks! It helped me a lot. Comprehensive yet simple to understand.

[@Zandeerline]

This is awesome! Thank you so much for providing this video. Never thought I would enjoy content on psm so much :)

[@harini7393]

The Best explanation ever!

[@adrianaguimaraes2973]

I loved it! Is there a second part?

[@NerdJake]

This is so great how you explained it! It would be even better without the music

[@upamanyumum]

Thanks for explaining this topic with such simplicity!!

[@stephaniekylebailey970]

This is awesome, do you have any videos explaining inverse probability of treatment weighted?

[@rafaelguinan3324]

Muy bueno. GRACIAS

[@heidicruz4457]

thank you too much for this easy to understand video.

[@MB-yg9vg]

wow ! love this video! thank you so much it made these concepts seem so much simpler!

[@kikimiyazaki5577]

Very simple and instructive. Thank you very much.

[@salomerutere5319]

extremely helpful!

[@pricillajeyapaul]

Thank you sis❤️

[@user-dominhtung]

Are you Vietnamese Emily?

[@lucisventusnoctis]

thank you this is the clearest example I found

[@aymantantawy1475]

wonderful

[@oooomar]

awesome!

[@mouhammadkanj6623]

Thank you. Very clear

[@0161rt]

Well explained!!!

[@tinayang5892]

this is so helpful!

[@benjy1898]

I get it now! Thanks so much!! :D

[@leticiamussin8436]

thank u

[@Pilarmedina777]

I LOVED IT!!! THANK YOU!

[@hippodino4965]

The example sounds not propensity score match but a fully blocked match.

[@yasinkhadem5963]

Yeah you're right it is not complete randomization, but she is not a statistician! generally it was OK i think.

[@jarvis1815]

nice one . i loved it

[@magilassalvacion5954]

Wow, great video. Thank you.

[@neusm90]

Thank you! So helpful and dinamic :)

[@hoang-thimorselli3925]

thank you so much, that was really helpful!

[@rohanvaswani9418]

How do variables such as: age and gender for example, affect whether a person gets treatment (smokes or doesn't smoke)?

[@amalfarooqi2379]

age, gender and other confounders/covariates affect the outcome, i.e. the lung cancer. smoking is the exposure.

[@applehotful]

Thanks a lot !!

[@00bikeboy]

Great explanation, thanks. Oh, and you have very nice hands :-)

[@kbadrig]

Thanks so much... I finally get it :D

[@girish869]

thank you so much very very helpfull mam