understanding-causal-inference Blog Related Topics 
Cloudera
DECEMBER 3, 2021
In part 1 of this blog post, we discussed the need to be mindful of data bias and the resulting consequences when certain parameters are skewed. Systems should be designed with bias, causality and uncertainty in mind. Even if protected features are removed, they can often be inferred from the presence of proxy features.
The Unofficial Google Data Science Blog
SEPTEMBER 23, 2015
by KAY BRODERSEN For the first time in the history of statistics, recent innovations in big data might allow us to estimate fine-grained causal effects, automatically and at scale. How can we establish that there is a causal link between our idea and the outcome metric we care about? Causal inference at scale may be one such key.
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Sisense
JANUARY 21, 2020
The paradox can be resolved by better understanding the data — exploring how it was generated and identifying the lurking variable. To better understand when the data should be grouped, you should be familiar with causal inference. This drug seems to be bad for women, bad for men, but good for people!”.
The Unofficial Google Data Science Blog
MARCH 13, 2017
By MUKUND SUNDARARAJAN, ANKUR TALY, QIQI YAN Editor's note: Causal inference is central to answering questions in science, engineering and business and hence the topic has received particular attention on this blog. In this post, we explore causal inference in this setting via the problem of attribution in deep networks.
The Unofficial Google Data Science Blog
JULY 22, 2020
However, if one changes assignment weights when there are time-based confounders, then ignoring this complexity can lead to biased inference in an OCE. Just as in ramp-up, making inferences while ignoring the complexity of time-based confounders that are present can lead to biased estimates. Y_i=Y_i(t)$ if $T_i=t$.
The Unofficial Google Data Science Blog
JANUARY 31, 2017
We can estimate the causal effect of prominence as the difference in log odds of uptake between stories which were recommended and those which were eligible (i.e. The former approach will tend to produce more realistic outcomes, but can be harder to understand, and may not give us adequate data to assess unlikely decisions.
Cloudera
OCTOBER 27, 2020
In the past year, we’ve released research reports and prototypes exploring Deep Learning for Anomaly Detection , Causality for Machine Learning and NLP for Automated Question Answering. textbooks, and understanding potential applications and limitations by prototyping. . Evolving Research At Cloudera Fast Forward.
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