000			01074nam a2200205 4500
005			20260225095852.0
008			260225b |||||||| |||| 00| 0 eng d
020			_a9781119186847
037			_cTextual
040			_aRTL _cRTL
084			_qRTL
100			_aPearl, Judea _9751745
245			_aCausal inference in statistics: A primer
260			_aUnited Kingdom _bJohn Wiley & Sons, Inc. _c2016
300			_axvii, 136 p. _bIncludes bibliographical reference and index
520			_aCausality is central to the understanding and use of data. Without an understanding of cause–effect relationships, we cannot use data to answer questions as basic as "Does this treatment harm or help patients?" But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data.
700			_aGlymour, Madelyn _eCo-author _91116591
700			_aJewell, Nicholas P. _eCo-author _91116592
942			_2CC _n0 _cTEXL
999			_c1680200 _d1680200